[FULL] Article 3, Volume 1 Issue 2

Advanced production practices and performance: empirical evidence from Spanish plants

Author

Darkys Luján-García – (Universidad de Sevilla)

Pedro Garrido-Vega – (Universidad de Sevilla)

Bernabé Escobar-Pérez – (Universidad de Sevilla)

DOI:

10.26595/eamr.2014.1.2.3

Abstract

This paper presents an empirical study that analyses the effect of applying three Advanced Production Practices (APPs) (Total Quality Management-TQM, Just in Time-JIT, and Total Productive Maintenance-TPM) on business performance measured with financial and non-financial (or operational) indicators. The study was conducted on a sample of Spanish companies in the automotive components, electronics and machinery sectors that took part in the 3rd Round of the High Performance Manufacturing (HPM) Project. The results of an analysis using Partial Least Squares (PLS) show that only two of the nine implementation indicators for the APPs being analyzed (process emphasis in TQM, and JIT delivery by suppliers) are positively related with non-financial performance. No significant relationship was found with financial performance, or between operating and financial performance. However, it should be borne in mind that the small size of the sample used in this study only enables strong relationships to be detected; a larger sample would be required to detect moderate or weak relationships.

Keywords

1. Introduction

The need to improve their competitiveness has led companies to embark upon initiatives in the production area to enhance operations performance by optimizing the use of resources and reducing costs. These initiatives and actions have been perfected over time until they became what today are known as Advanced Production Practices (APPs). These then became widespread internationally. They represent broad concepts linked to productive activities, and in most cases, there is no consensus on their definition or their potentiality (Cua et al., 2006).

The most important APPs that have been studied and applied from the beginning of this revolution in Production Management include Total Quality Management (TQM), Just in Time (JIT) and Total Productive Maintenance (TPM), which some authors include among the pillars of World Class Manufacturing (Schonberger, 1986). Whether applied separately or, preferably, in an integrated way, these APPs have a positive impact on several areas of the company with valuable outcomes in a number of aspects on the plant level, such as: better customer satisfaction, reductions in the production cycle and a fall in delivery times, to mention but a few (Flynn et al., 1995; Abdel-Maksoud et al., 2005; Cua et al., 2006).

The degree to which these APPs are applied is measured using various “indicators” and/or “scales” which have become standardized and perfected over time (Flynn et al., 1995 and Cua et al., 2001, 2006). The relationship between APPs application and performance measured with operational and non-financial measures (NFI) has been addressed in depth in the specialized literature (Abdel-Maksoud et al., 2005). However, financial indicators (FI) have been less used in the context of APPs and the findings of the studies that have used them are not entirely conclusive (Fullerton and Wempe, 2009). The two types of indicator need to be considered jointly to measure performance if advances are to be made in the evaluation of APP implementation.

In keeping with the above, this paper conducts an empirical analysis of the effect of the three above-mentioned APPs on both operational, or non-financial, performance and financial performance, as well as of the relationship between the two types of performance.

The following section examines the antecedents to the research that supports the hypotheses, prior studies related to APP application and their relationship with both operational and financial performance indicators. The third section presents the methodology followed to conduct the study. Subsequently, the research findings are set out, and then discussed in Section five. Finally, the conclusions drawn from this study are presented along with future lines of research.

2. Antecedents and hypotheses

This section reviews prior research on indicators used for the application of the three APPs considered and their relationship with financial and non-financial performance.

The decision was taken to formulate the hypotheses from a positive point-of-view, as, in economic terms, it is logical to suppose that companies invest in APPs, such as TQM, JIT/LM and TPM, amongst others, in the pursuit of improvements in the efficacy and efficiency of their processes and, therefore, of their performance, irrespective of how it is measured. In this way, they will at least recover the investments that they have made.

2.1. Total Quality Management (TQM)

TQM is a holistic focus production practice designed to improve effectiveness and operating efficiency. It involves the entire organization and focuses on complying with and surpassing customer expectations (Dahlgaard et al., 2007; Kumar et al., 2011). Numerous studies can be found in the literature that analyze the implementation of this APP and its effect on both operating/non-financial and financial performance obtained by companies (e.g., Flynn et al., 1995; Agus et al., 2000; Agus, 2005; Cagwin and Barker, 2006; Demirbag et al., 2006; Yeung et al., 2006).

Among the main indicators, constructs and scales that have been used to measure the implementation of this APP are: continuous improvement and learning, customer focus, customer involvement, customer satisfaction, feedback, company-wide focus, preventive process control, process emphasis, supplier alliances, supplier quality improvement, top management leadership for quality, TQM–customer link, problem- solving and supplier quality level teams.

Logically, no study uses all of these indicators, but rather a group of them depending on the purpose of the research. Amongst the most used are customer involvement, supplier quality improvement and process emphasis, and these are the indicators that will be used in this study. It will be noted that these three indicators focus on the three major phases of the productive process: suppliers, the process per se and customers, thus providing an overview of TQM.

2.1.1. Customer Involvement (CI)

Customer Involvement, or Customer Focus, as some authors prefer to call it (Ahmad and Schroeder, 2002; Sila and Ebrahimpour, 2005), is one of the most used indicators in the context of TQM implementation (e.g., Powell, 1995; Curkovic et al., 2000; Cua et al., 2001, 2006). Some studies have proven that customer involvement affects both operational/non-financial -flexibility, cost, delivery, etc. (Cua et al., 2001, 2006) and financial investment performance, market share, etc. (Curkovic et al., 2000) performance positively. There are also some studies, such as Sila and Ebrahimpour (2005), that find no relationship between Customer Focus and the business results, measured using non-financial indicators (productivity, cycle times and number of errors or defects, among others) and financial indicators (profit and ROA, among others).

The following hypotheses were proposed in line with earlier studies:

H1a: There is a positive relationship between CI and operational or non-financial performance.

H1b: There is a positive relationship between CI and financial performance.

2.1.2. Supplier Quality Improvement (SQI)

This is another important indicator in the context of TQM implementation (Flynn et al., 1995; Cua et al., 2001, 2006; Kaynak, 2003; Sila and Ebrahimpour, 2005). Abusa and Gibson (2013) have proven that it is positively related to performance indicators, such as the defect rate, increased sales and increased profit. However, Sila and Ebrahimpour (2005) again found no significant relationship between this indicator and the business results.

The following hypotheses can therefore be formulated:

H2a: There is a positive relationship between SQI and operational or non-financial performance.

H2b: There is a positive relationship between SQI and financial performance.

2.1.3. Process Emphasis (PE)

The third indicator used in the context of TQM is process management emphasis (Saraph et al., 1989; Claver et al., 2003; Cua et al., 2001, 2006; Prajogo and Sohal, 2006; Abusa and Gibson, 2013). Proper process management improves non-financial indicators, such as the product defect rate (Saraph et al., 1989; Claver et al., 2003; Abusa and Gibson, 2013) and delivery time (Cua et al., 2001). However, Samson and Terziovski (1999) do not find that this indicator has a positive effect on the organization’s performance measured through customer satisfaction, productivity, percentage of defective products, quality costs, etc. In light of the above, the following hypotheses are proposed:

H3a: There is a positive relationship between PE and operational or non-financial performance.

H3b: There is a positive relationship between PE and financial performance.

2.2. Just In Time (JIT)/Lean Manufacturing (LM)

Just in time (JIT) was first implemented in the Toyota Motor Company and then spread throughout the West in the nineteen-eighties (Singh and Singh, 2013). JIT philosophy is basically aimed at eliminating wastage, understood as anything and everything that adds cost to the product but no value (Schonberger, 1982). Specifically the main sources of wastage, as defined in JIT, include excess inventory, scrap and reprocessing (Brox and Fader, 2002).

Some authors currently consider JIT as the core of a wider APP, Lean Manufacturing (Bortolotti et al., 2013; Klingenberg et al., 2013). Much research has been carried out into applying JIT/LM and their impact on operational/non-financial performance and financial performance (Boyd,2001; Callen et al., 2003; Inman et al.,2011).

In a literature review, Mackelprang and Nair (2010) found a total of ten indicators that have been commonly used to measure JIT: reduced lead time, small lot size, JIT delivery by suppliers, keeping to a daily schedule, preventive maintenance, equipment layout, the Kanban system, the JIT-customer link, the pull system and the repetitive nature of the master program.

The three most classic indicators from those above, used in over 90% of the studies analyzed, will be used in this research: the Kanban system, equipment layout and JIT delivery by suppliers.

2.2.1. Just in Time Delivery by Suppliers (JTDS)

There are a great number of studies (Forza, 1996; Callen et al., 2000; Shah and Ward, 2003; Das and Jayaram, 2003; Ketokivi and Schroeder, 2004; Swink et al., 2005; Li et al., 2005; Narasimhan et al., 2006; Avittathur and Swamidass, 2007; Matsui, 2007; Dal Pont et al., 2008) that have used this indicator to measure JIT implementation. These papers have examined the effect of JTDS on five non-financial (operational) performance indicators indiscriminately: inventory level, cycle time, deliveries, quality, cost and flexibility. Basing themselves on these earlier studies, Mackelprang and Nair (2010) proved that JTDS has a medium impact on the above-mentioned performance indicators. Phan & Matsui (2010) found that the relationship between JIT production practices and plant performance was contingent on the national context and infrastructure practices in quality and workforce management. In particular, JTDS was correlated with some of the five performance indicators (cost, on-time delivery, volume flexibility, inventory turnover and cycle time) but not all in all countries.

However, no study has been found that verifies the relationship with the company’s financial results. In spite of this, the following hypotheses are proposed:

H4a: There is a positive relationship between JTDS and operational or non-financial performance.

H4b: There is a positive relationship between JTDS and financial performance.

2.2.2. Kanban System

Fifteen studies in all have been found in the bibliography that use this indicator in the context of JIT (Sakakibara et al., 1997; Lieberman and Demeeter, 1999; Fullerton and McWatters, 2001; Fullerton and McWatters, 2002; Fullerton et al., 2003; Callen et al., 2003; Christiansen et al., 2003; Ahmad et al.,2004; Cua et al.,2001, 2006; Ward and Zhou, 2006; Matsui, 2007; Bayo-Moriones et al., 2008; Inman et al., 2011; Danese et al., 2012). One of the main findings is that Kanban has a significant effect on advanced manufacturing technologies (AMT), basic quality tools and the management of vertical relationships (Bayo-Moriones et al., 2008). Meanwhile, Danese et al. (2012) proved that implementing JIT production (using the Kanban system as one of the indicators to measure its implementation) is directly related with enhanced delivery performance.

Fullerton et al. (2003) found increasing marginal returns to long-term JIT investment for JIT practices such as Kanban and JIT purchasing in a time-series model. However, they found an insignificant association in a full cross-sectional model. This suggests that the benefits of these JIT practices are realized only over time and that they are negatively associated with profit in some stages of JIT adoption.

In line with the above, the following hypotheses are formulated:

H5a: There is a positive relationship between the Kanban system and operational or non-financial performance.

H5b: There is a positive relationship between the Kanban system and financial performance.

2.2.3. Equipment Layout (EL)

Cua et al. (2001) and Mackelprang and Nair (2010) have used this indicator to measure the effects of JIT implementation. Cua et al. (2001) use it along with four further items to measure JIT implementation. They conclude from the study that EL is not significantly related to non-financial/operational performance (measured through cost efficiency, conformance quality, on-time deliveries and volume flexibility) either when the implementation of the APP is analyzed on its own, or when contingency factors are taken into account. Meanwhile, more recently Mackelprang and Nair (2010) conducted a meta-analysis of the relationship between JIT and operating performance (measured through cycle time, deliveries, quality, cost and flexibility) and found eight articles published in journals in the areas of operations management, management, marketing and logistics from 1992 to 2008 that use EL to evaluate the implementation of JIT. Based on these studies, the finding is that EL has a medium impact on operating/non-financial performance, although it is not always significant.

No research study was found that examines the relationship of this indicator with financial performance, therefore the following hypotheses are proposed:

H6a: There is a positive relationship between EL and operating or non-financial performance.

H6b: There is a positive relationship between EL and financial performance.

2.3. Total Productive Maintenance (TPM)

Total productive maintenance (TPM) was originally developed in Japan and is based on a preventive system that involves all levels of the plant, from the Plant General Manager to the shop floor worker. Its application has proved to be a success, as it results in greater productivity and an increase in the efficiency of production equipment (Keung, 2003).

Some of the classic indicators used to assess TPM implementation are autonomous maintenance, preventive maintenance and maintenance support.

2.3.1. Autonomous Maintenance (AM)

McKone et al. (2001), Ahuja and Khamba (2008) and Lazim et al. (2013) have used this indicator to evaluate TPM implementation on the plant level. Ahuja and Khamba (2008) demonstrated that TPM implementation has fostered autonomous maintenance. This is reflected in improvements in some aspects, such as the elimination of waste, improvements in the reliability of manufacturing processes and cost reductions. Meanwhile, Lazim et al. (2013) state that autonomous maintenance-related activities result in large reductions in manufacturing costs (including production costs, labor, and general materials and unit costs).

The following hypotheses are proposed on the basis of the above:

H7a: There is a positive relationship between AM and operational or non-financial performance.

H7b: There is a positive relationship between AM and financial performance.

2.3.2. Preventive Maintenance (PM)

This is another major indicator to be taken into account when implementing TPM (Nakazato, 1994; Abdallah, 2013). Nakazato (1994), specifically, evaluates it through daily maintenance and periodic maintenance. Swanson (2001) demonstrated that proactive/preventive maintenance was positively related with improvements to product quality, improvements in equipment availability, and reduction in production costs. Konecny and Thun (2011) found that TPM implementation was positively related with non-financial performance (quality, cost, time and flexibility), but that the Preventive Maintenance indicator was the weakest of the three indicators used to evaluate TPM.

On the basis of the above the following hypotheses are proposed:

H8a: There is a positive relationship between PM and operational or non-financial performance.

H8b: There is a positive relationship between PM and financial performance.

2.3.3. Maintenance Support (MS)

Another of the indicators that is usually used to measure TPM is the support or aid given to the maintenance function (Abdallah, 2013). This indicator refers to issues such as the setting of maintenance standards, the management of replacement parts and systems for information on equipment breakdowns. Although it is an important indicator in the context of TPM, no studies have been found that evaluate its effect on financial and non-financial performance. Given the foregoing, the following hypotheses are proposed for testing:

H9a: There is a positive relationship between MS and operational or non-financial performance.

H9b: There is a positive relationship between MS and financial performance.

2.4. The relationship between the non-financial operational indicators and the financial indicators

According to Baines and Langfield-Smith (2003), most research states that a great deal of confidence is placed on the information provided by financial and non-financial indicators to evaluate both past and prospective activities. However, the relationship between these two types of indicator is very ambiguous and there is no precise knowledge of what the real interaction between them is.

To be specific, some research studies have been found that examine the relationship between the two types of indicator on the basis of the application of some APPs. Ittner and Larcker (1995) found that a greater use of non-financial indicators is linked to improvements in financial performance both when quality programs are formalized and in environments where they are not. Perera et al. (1997) concluded that there is an increase in the use of non-financial indicators in companies that adopt advanced manufacturing practices. Nonetheless, they found no link with financial performance measured as an increase in the sales rate, net profit/revenue and return on assets. Meanwhile, Callen et al. (2000) found that non-financial indicators were not related to profit either at plants that implemented JIT or those that did not.

Other studies state that there is a positive relationship between these two types of indicator. Such is the case of Durden et al. (1999), Baines and Langfield-Smith (2003) and Said et al. (2003), who state that a greater use of non-financial information is linked with improvements to financial indicators. More recent studies, such as Fullerton and Wempe (2009) and Hofer et al. (2013), test for the existence of a mediating or moderating effect of non-financial indicators between the implementation of APPs and the financial results.

Despite the lack of consensus found in the prior literature regarding the relationship between operational and financial indicators, it is reasonable to suppose that if improvements are made to the former -a reduction in the number of defective products or a shorter response time, for example- this would have a knock-on effect on income and, more especially, on costs, and consequently, also on the economic-financial result and, evidently, the company’s performance. Thus, the last hypothesis to be tested is as follows:

H10: There is a positive relationship between operational or non-financial performance and financial performance.

3. Methodology

Data from the International High Performance Manufacturing (HPM) Project will be used in this empirical study. The objective of this project is to use an extensive survey to analyze the factors that contribute to the success of high performance manufacturers (Schroeder and Flynn, 2001; Hallgren and Olhager, 2009). To be precise, there are 12 questionnaires that contain information on all plant levels and are administered to 21 informants in the study (10 senior management, 6 supervisors and 5 production workers). These questionnaires contain hundreds of questions, most of which are scored using perceptual scales.

Information for this article has been taken from the database relating to indicators of the implementation of the aforementioned APPs and to operational (non-financial) performance corresponding to the Spanish plants that took part in the 3rd Round. In this project, a stratified design was used to randomly select an approximately equal number of plants (with at least 100 workers) across three industrial sectors in each country. Specifically, the sample used in this study is made up of a total of 20 plants in the auto components, electronics and machinery sectors. The sample size qualifies it as a borderline sample (i.e., one where the size is just adequate to satisfy a statistical power analysis) that requires care on the part of the researchers when choosing the tools for the analyses and when making interpretations (Avittathur and Swamidass, 2007).

Two of the most commonly used indicators in previous research were used to evaluate operational/non-financial (NFI) performance: delivery time and flexibility to change the product mix (e.g., Sim, 2001; Ahmad et al., 2004).

The financial data used in the study were taken from the SABI (the Iberian Balance Sheet Analysis System) commercial database, as the HPM 3rd Round database does not contain sufficient financial data. The financial indicator taken for the study was return on sales (ROS) for the 2007 tax year. This performance indicator is very important for Fullerton and Wempe (2009) as it is (1) widely accepted as a measure of financial performance; (2) has proven to be a determinant of improved return on assets (ROA) for companies that adopt JIT (Kinney and Wempe, 2002); and (3) it eliminates some of the confusion that inventory reductions cause for ROA. The study will also be conducted with Cash Flow Margin or EBITDA Margin (EBITDA/Net Sales) to see whether the results that are obtained are similar.

In a similar way to Fullerton and Wempe (2009), this study aims to analyze a model that relates the APP indicators with the NFI and with ROS (Table 1).

Advanced Production Practices (APPs)	*TQM* Customer involvement (CI) Supplier quality improvement(SQI) Process emphasis(PE)	*JIT* Kanban system(Kanban) Just-in-Time Delivery by Suppliers (JTDS) Equipment layout (EL)
	*TPM* Autonomous maintenance (AM) Preventive maintenance (PM) Maintenance support (MS)
Performance indicators	*Non-financial indicators (NFI)* On time delivery Flexibility to change product mix Flexibility to change volume		*Financial indicator* Return on Sales (ROS)

Table 1: Implementation indicators used for each APP and performance indicators.

Annex 1 provides details of all the items included in the questionnaires that make up the indicators of the three APPs (TQM, JIT/LM and TPM) and the respective loads obtained in factor analysis. As can be seen, all the items present suitable loads (over 0.4 are considered important according to Hair et al., 1999) on their respective indicators or constructs.

The APPs will be analyzed separately due to the small size of the sample. There are therefore three research models to be tested (see Figure 1).

Figure 1a: TQM model

Figure 1b: JIT model

Figure 1c: TPM model

Figure 1: Relationship models by APP.

Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to evaluate the models and test the hypotheses using Smart PLS statistical software. PLS-SEM procedures have recently gained great acceptance in studies on business management and the economy (Hair et al., 2011). There are already some examples specifically linked to operations management, such as Hartmann and De Grahl (2012) and Kim et al. (2013) that address topics linked to the outsourcing of logistics services, and supply chain management, respectively. Meanwhile, PLS is a technique that enables very small samples to be worked with, as is the case of this research, and it places few prior statistical assumptions on the data.

The use of this technique involves two phases (Barclay et al., 1995). Phase 1 refers to the evaluation of the measurement model for the validity and reliability of the constructs. In phase 2, the structural model is evaluated and the proposed hypotheses are tested (Henseler et al., 2009).

4. Results

4.1. Phase 1: Evaluation of measurement model (outer model)

The main results of Phase 1 are given in Table 2, which shows the measurement model quality criteria for each of the models. Firstly, the composite reliability (CR) index was used for the reliability analysis. The score for this indicator must be greater than 0.7 for the construct to be reliable (Nunnally and Bernstein, 1994). The scores in the last column of Table 2 show that all the constructs possess a suitable level of reliability as they are all greater than 0.7. Secondly, the convergent validity is evaluated. This is shown by average variance extracted (AVE) (Fornell and Larcker, 1981). The AVE scores must be over 0.50 for the indicator to be valid (Henseler et al., 2009; Hair et al., 2011). As the Table shows, all the constructs present convergent validity as all the AVE scores exceed 0.519. Thirdly, the degree to which any given construct differs from the other constructs was also tested, i.e., discriminant validity. Following the Fornell-Lacker (1981) criterion, the square root of the AVE values should be greater than the latent variable correlations (not shown in Table 2). This criterion is met for all the constructs.

TOTAL QUALITY MANAGEMENT (TQM)
	CR	AVE	Discriminant Validity?
CI	0.917	0.735	YES
PE	0.846	0.650	YES
SQI	0.891	0.672	YES
NFI	0.831	0.621	YES
JUST IN TIME (JIT)
	CR	AVE	Discriminant Validity?
JTDS	0.803	0.580	YES
EL	0.879	0.646	YES
Kanban	0.933	0.822	YES
NFI	0.817	0.602	YES
TOTAL PRODUCTIVE MAINTENANCE (TPM)
	CR	AVE	Discriminant Validity?
AM	0.918	0.789	YES
MS	0.804	0.579	YES
PM	0.812	0.519	YES
NFI	0.826	0.614	YES

Table 2: Measurement model quality criteria.

4.2. Phase 2: Evaluation of the structural model (inner model)

This section gives the results of hypothesis testing once the measurement model used has been satisfactorily evaluated. The first criterion for evaluating a PLS-SEM is to evaluate the determination coefficient (R²) of the endogenous constructs (Hair et al., 2011). The value of R²represents a measure of the model’s capacity for prediction (Henseler et al., 2009). Falk and Miller (1992) recommend that R²should be at least greater than 0.10. The R² values exceeded the permitted maximum; despite this, high values were not recorded for the two endogenous variables (ROS and NFI) in any of the models under study (see Table 3).

A non-parametric re-sampling technique (bootstrapping) is used (with 2000 samples) to examine the statistical significance of the estimations obtained. Table 3 shows the path coefficients and the t-student statistical test that enable the hypotheses to be tested. The path coefficients must be positive and the t-student scores must be greater than 1.646 (value corresponding to a one-tailed, asymmetrical test of significance α=0.05) for the hypotheses to be supported and for the relationships to be significant.

TOTAL QUALITY MANAGEMENT (TQM)
Relationships	Path coefficients	t –student	R²	HYPOTHESIS SUPPORTED?*
H1a-CI -> NFI	-0.431	1.123		NO
H1b-CI ->ROS	0.103	0.253		NO
H2a-SQI -> NFI	-0.045	0.131		NO
H2b-SQI -> ROS	-0.162	0.578		NO
H3a-PE -> NFI	0.566	2.089*		YES
H3b-PE -> ROS	-0.370	1.072		NO
H10a- NFI_TQM -> ROS_TQM	0.362	0.910		NO
Endogenous variables	*NFI_TQM*		0.289
Endogenous variables	*ROS_TQM*		0.204
JUST IN TIME (JIT)
Relationships	Path coefficients	t –student	R²	HYPOTHESIS SUPPORTED?*
H4a-JTDS -> NFI	0.697	2.153*		YES
H4b-JTDS -> ROS	0.334	0.663		NO
H5a-Kanban -> NFI	-0.104	0.406		NO
H5b-Kanban -> ROS	-0.407	1.527		NO
H6a-EL -> NFI	-0.061	0.191		NO
H6b-EL -> ROS	0.037	0.092		NO
H10b-NFI_JIT-> ROS_JIT	0.119	0.321		NO
Endogenousvariables	*NFI_JIT*		0.389
Endogenousvariables	*ROS_JIT*		0.234
TOTAL PRODUCTIVE MAINTENANCE (TPM)
Relationships	Path coefficients	t –student	R²	HYPOTHESIS SUPPORTED?*
H7a-AM -> NFI	0.072	0.199		NO
H7b-AM -> ROS	0.492	1.317		NO
H8a- PM -> NFI	0.106	0.351		NO
H8b-PM -> ROS	0.141	0.369		NO
H9a-MS -> NFI	0.373	1.141		NO
H9b-MS -> ROS	-0.480	1.423		NO
H10c-NFI_TPM -> ROS_TPM	0.321	0.984		NO
Endogenousvariables	*NFI_TPM*		0.229
Endogenousvariables	*ROS_TPM*		0.438

*p < 0.05 (based ont (1999) = 1.6456, one-tailed test)

Table 3: Hypothesis testing.

5. Discussion

In general terms, the results of this study are in line with prior studies that find no clear relationship between the implementation of these APPs and performance, financial performance especially.

With respect to the a hypotheses that refer to operational or non-financial (NFI) indicators, only TQM dimension, Process Emphasis (PE) (H3a) and Just-in-Time Delivery by Suppliers (JTDS) (H4a) as a dimension of JIT, have been found to be significantly related to operational performance, whilst all the other indicators show non-significant relationships that are even negative on occasion. This result is partially in keeping with the findings of Sila and Ebrahimpour (2005) and Fullerton et al. (2003), to mention only two studies. However, Cua et al. (2006) found that the joint implementation of TQM, JIT and TPM (not examined in this study due to the sample size) is linked to higher levels of operational performance. We believe that the following circumstances should be taken into account for a better interpretation of these findings:

The size of the sample is too small as we were only able to work with data from 20 Spanish plants. This does not allow for the necessary statistical power to detect effects of moderate or small size.
As there is no consensus on the definition and scope of the APPs’ implementation indicators, the way in which their application has been measured might influence the results. Three indicators have been used per APP in this study, and these were chosen from those that were found to be the most used in a literature review. Nonetheless, other indicators or dimensions of APPs exist that might yield different findings.
The fact that operational performance has been considered as a single dimension, as a single construct, could be obscuring other positive results that the application of APPs could have on the individual operational performance aspects considered (on-time delivery and flexibility to change the product mix) or on other aspects that have not been included (cost, speed, quality, etc.). A future analysis could examine the effect of the APPs on the various aspects of operational performance separately.
The success of APP implementation depends above all on contingent factors (e.g., company size, number of employees, etc.) not considered in this research that impact on the success of their application.

With regard to the b hypotheses, which link the implementation indicators for each APP with financial performance as a dependent variable, the results are partially in line with Ittner and Larcker (1995) and Fullerton et al. (2003). No significant results were found between any of the APP implementation indicators and ROS, and some of the coefficients are even negative. Therefore, the results for ROS are even worse than for NFI as far as the hypotheses being supported is concerned. When interpreting these results it has to be borne in mind that APP application has been measured on the plant level, while the financial performance indicator is measured at the company level. This might be the reason why the results do not show the real relationship between the implementation indicators for each APP and financial performance, and it will therefore be important to distinguish between the two units of analysis in future studies. In addition, it should be taken into account that ROS, like any other measure of financial performance, is affected by many other company factors, which do not come under the direct responsibility of the Production Manager. This raises an issue that cannot be solved easily, as this circumstance affects any measure of financial performance. In fact, the models were recalculated using the EBITDA Margin and the results were very similar.

Finally, no relationship was found between NFI and ROS (H10a, b and c) in any of the models for the different APPs. As stated in Section 2, this is a relationship that has been less studied by the academic community. This result is in line with prior studies, such as Callen et al. (2000), although there are other studies, such as Fullerton and Wempe (2009) and Hofer et al. (2013) that have found a direct effect between non-financial performance and ROS. In this case, however, despite the results not being significant, the coefficients are positive in the three models analyzed.

6. Final considerations and future research

This paper presents an empirical study that analyses separately the effect of implementing three main APPs (TQM, JIT/LM and TPM) on performance. Three different models, one for each of the APPs, with first-order reflective constructs were tested. Each APP was measured using three related implementation indicators. As performance is a very broad and diverse concept, it has been measured in this study using both non-financial and financial indicators. Non-financial indicators are the indicators par excellence for analyzing APP performance, whilst the financial indicators add valuable information on the performance of the APPs from the financial/accounting perspective. The indicators complement each other and provide the required information about the performance obtained from the APPs. The study was conducted in 20 Spanish plants in the machinery, automotive components and electronics sectors.

Measuring the implementation of these broad APPs that are multi-dimensional and complex and of performance itself is difficult in both cases, as witnessed by the generalized lack of agreement in this respect. Although companies make enormous efforts and investments to implement APPs with the intention of improving their performance and competitiveness, the relationship between the two variables is still difficult to grasp, in spite of all the papers that have been published. This study makes an empirical contribution to our knowledge of this relationship, which is very important for companies and also arouses great interest in the scientific community.

The results of the study indicate that the application of the APPs was related, but to a limited extent, to non-financial performance, specifically Delivery by Suppliers for JIT, and Process Emphasis for TQM. However, no relationship was found with financial performance, in this case, ROS. In general terms the results are in line with a part of the extant literature. It is supposed that the operating indicators are those that are most directly related with the APPs. However, despite financial performance depending on many other factors, apart from these APPs, some significant relationship might have been anticipated. Nevertheless, as already stated in the section 5, these results could have been affected by the small sample size.

As future research, the intention is to reproduce this study, but with a larger sample and also including plants from other both developed and emerging countries. The number of indicators to be used could also be increased both to evaluate the effective implementation of the APPs and to measure financial and non-financial performance. Finally, although it is more difficult to analyze, perhaps it would be appropriate to bear in mind the time delay in financial results when improvement programs are applied, in this case, the implementation of APPs.

Acknowledgments

This research has been partly funded by the Spanish Ministry of Science and Innovation, project DPI-2009-11148, and by the Junta de Andalucía project P08-SEJ-03841. The authors wish to acknowledge both Governments’ support.

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ANNEX 1

Indicators ofAdvanced Production Practice implementation.

Total Quality Management (TQM)

Constructs	Item description	Factor loading
Customer Involvement	We frequently are in close contact with our customers	0.927
	Our customers give us feedback on our quality and delivery performance.	0.845
	Our customers are actively involved in our product design process	0.715
	We regularly survey our customers’ needs.	0.925
Supplier Quality Involvement	We strive to establish long-term relationships with suppliers.	0.925
	Our suppliers are actively involved in our new product development process.	0.817
	We maintain close communication with suppliers about quality considerations and design changes.	0.758
	We actively engage suppliers in our quality improvement efforts	0.769
Process Emphasis	We believe that the process, rather than the people performing the process, is the source of most errors.	0.810
	In our view, most problems result from the production system, rather than from individual employees.	0.905
	In our view, the process is the entity that should be managed.	0.689
Non-financial indicators	On time delivery performance	0.748
	Flexibility to change product mix	0.806
	Flexibility to change volume	0.808

Just in Time/Lean Manufacturing (JIT/LM)

JIT Delivery by Suppliers	Our suppliers deliver to us on a just-in-time basis.	0.696
	We receive daily shipments from most suppliers	0.668
	Suppliers frequently deliver materials to us.	0.900
Kanban	We use a kanban pull system for production control.	0.946
	We use kanban squares, containers or signals for production control.	0.933
	Suppliers fill our kanban containers, rather than filling purchase orders.	0.838
Equipment Layout	The layout of our shop floor facilitates low inventories and fast throughput.	0.689
	Our processes are located close together, so that material handling and part storage are minimized.	0.809
	We have located our machines to support JIT production flow.	0.868
	We have laid out the shop floor so that processes and machines are in close proximity to each other.	0.837
Non-financial indicators	On time delivery performance	0.625
	Flexibility to change product mix	0.847
	Flexibility to change volume	0.8357

Total Productive Maintenance (TPM)

Autonomous Maintenance	Cleaning of equipment by operators is critical to its performance.	0.764
	Basic cleaning and lubrication of equipment is done by operators.	0.959
	Operators inspect and monitor the performance of their own equipment.	0.929
Preventive Maintenance	We upgrade inferior equipment, in order to prevent equipment problems.	0.668
	In order to improve equipment performance, we sometimes redesign equipment.	0.692
	We use equipment diagnostic techniques to predict equipment lifespan.	0.752
	We do not conduct technical analysis of major breakdowns.	0.767
Maintenance Support	Our production scheduling systems incorporate planned maintenance.	0.833
	Equipment performance is tracked by our information systems.	0.699
	Our systems capture information about equipment failure.	0.746
Non-financial indicators	On time delivery performance	0.855
	Flexibility to change product mix	0.764
	Flexibility to change volume	0.726

[FULL] Article 2, Volume 1 Issue 2

Corporate governance and performance during plummeting and soaring financial markets

Author

Kurt A. Desender – (Universidad Carlos III)

Mónica Lopez Puertas-Lamy – (Universidad Carlos III)

DOI:

10.26595/eamr.2014.1.2.2

Abstract

The objective of this study is to investigate the relationship between stock market performance and ownership structure during plummeting and soaring financial markets in a Continental-European setting. Our results demonstrate the importance of ownership concentration, the presence of multiple blockholders and the type of ownership to explain stock market performance in periods of stock market turbulence. In addition, we find that the results for extreme down markets are fundamentally different from the up-market results. While ownership concentration is valued positively during down market periods, it is valued negatively during up market periods. Furthermore, the presence of multiple blockholders only influences the stock price during down market periods. Finally, firms controlled by financial institutions lose less value during down markets, while firms controlled by non-founding families lose more value, compared to widely held firms, during down market periods. No significant relationship is found during up market periods.

Keywords

1. Introduction

In nearly 200 years of recorded stock-market history, no calendar decade has seen such a dismal performance as the 2000s. Stock markets worldwide have experienced severe shocks, causing extreme losses and gains in very short periods of time. For example, as a result of the internet bubble bursting, the S&P500 lost 15 percent over the period of 10 days in July 2002, while other mayor stock market indexes suffered similar losses. This study looks at extreme up and down market periods over the last decade to understand how investors value the ownership structure during periods of market turmoil.

The ownership structure is one of the main dimensions of corporate governance. Shareholder structures are quite diverse across countries, with dispersed ownership being much more frequent in US and UK listed firms, compared to Continental Europe, where controlled ownership is prevalent (La Porta, López-de-Silanes, Shleifer and Vishny, 1999). Faccio and Lang (2002) report in a study of 5232 publicly traded corporations in 13 Western European countries that only 36.93 percent were widely held firms. In addition, cross-country studies of La Porta et al. (1999) point out that ownership of large companies in rich economies is typically concentrated; that control is often exercised through pyramidal groups with a holding company at the top controlling one or more subsidiaries; and that the controlling shareholders are often actively involved in company management and sit on the board of directors. We refer to Enriques and Volpin (2007) for a detailed description of the differences in the ownership structure of companies in the main economies of continental Europe with comparisons to the United States and the United Kingdom. The differences in ownership structure have two obvious consequences for corporate governance, as surveyed in Morck, Wolfenzon, and Yeung (2005). On the one hand, dominant shareholders have both the incentive and the power to discipline management. On the other hand, concentrated ownership can create conditions for a new problem, because the interests of controlling and minority shareholders are not aligned. Since ownership control can have both positive and negative properties, empirical evidence is of paramount importance for judging about its final effect and for orienting regulations that could hamper the persistence of large controlling shareholders.

Previous literature has extensively focused on the relationship between ownership structure and firm performance. However, far less attention has been paid to the influence of ownership structure on stock price performance, i.e. the shareholder valuation of the ownership structure. Thomsen, Pedersen and Kvist (2006) argue that if the exchange price of a firm’s stock measures its value for minority investors, the impact of blockholder ownership on this stock value reflects the net costs or the benefits of large owners from a minority investor viewpoint. As explained by Dyck and Zingales (2004, p. 52), the potential extraction of private benefits by controlling shareholders “reduces what minority shareholders are willing to pay for shares, lowering the value of all companies where such behavior represents a real possibility. And by raising the cost of finance, it limits the ability of such firms to fund attractive investment projects.” The few studies available on the relationship between stock price performance and the ownership structure have focused on the East-Asia crises (Baek et al., 2004; Mitton, 2002). Overall, their findings suggest that differences in the ownership structure play an important role in determining changes in firm value during periods of financial crisis.

The objective of this study is to investigate the relationship between stock market performance and ownership structure during plummeting and soaring financial markets in a Continental-European setting. This setting is interesting to investigate the influence of ownership structure on stock price performance because it of its institutional contrast with Anglo-American economies. While Continental European institutional characteristics include high concentration of ownership, weak investor rights, fewer market-oriented rules for disclosure, weaker managerial incentives and greater supply of debt, the United Kingdom and United States are characterized by dispersed ownership where markets for corporate control, legal regulation, and contractual incentives are key governance mechanisms. In addition, to obtain greater insight into the valuation of the ownership structure from a (minority) shareholders perspective, it is crucial to have sufficient diversity in terms of ownership structure in the sample, a condition fulfilled in most continental Europe stock markets.

A common critique of the ownership-performance literature is that corporate ownership is an endogenous variable rather than an exogenous influence on firm profitability (Demsetz and Lehn, 1985). Ownership is likely to be endogenous when shareholders have sufficient information about the future performance variability of the companies in which they invest, and when they can adjust the size of their shareholdings at no cost. Ownership in Continental European firms is relatively stable, in part due to the fact that the restructuring of ownership is costly and difficult since the stock market is relatively less developed. In these markets, ownership can be treated as an exogenous variable, as investors can neither acquire nor divest controlling blocks without incurring significant costs, and must therefore seek to maximize the performance of corporations given the block of shares they own in them (Stiglitz, 1994). In other words, the Continental European context allows us to sidestep the endogeneity critique of the ownership literature. In addition, using a given set of measures for ownership structure immediately before the crisis to explain posterior changes in the stock prices, while controlling for changes during the considered period, largely eliminates any spurious causality.

Our results demonstrate the importance of ownership concentration, the presence of multiple blockholders and the type of ownership to explain stock market performance. In addition, the results for extreme down markets are fundamentally different from the up market results. While ownership concentration is valued positively during down market periods, it is valued negatively during up market periods. Furthermore, the presence of multiple blockholders only influences the stock price during down market periods. Finally, firms controlled by financial institutions lose less value during down markets, while firms controlled by non-founding families loose more value, compared to widely held firms, during down market periods. No significant relationship is found during up market periods.

This study builds on prior research in several ways. First, unlike most existing research, which usually studies just one aspect of ownership structure, we focus on several dimensions of the ownership structure: ownership concentration, multiple blockholders and type of ownership. Second, rather than focussing on periods of market crisis, we analyse the stock market performance not only during extreme down market periods, but also look at extreme up market periods. Third, this is one of the first papers to investigate the importance of ownership structure from an investor’s perspective using data from a Continental European stock market.

In the next section, we revise the prior literature and develop our hypotheses. Then, we discuss our sample and data collection, followed by an overview of our methodology and variable specification. The final two sections discuss our results and provide a final conclusion.

2. Prior literature and hypotheses development

Shareholder structures are quite diverse across countries, with dispersed ownership being much more frequent in US and UK listed firms, compared to Continental Europe, where controlled ownership is prevalent (La Porta, et al., 1999). An important weakness of dispersed ownership is that dispersed owners lack both the means and the motive to address managerial agency problems. In the presence of information asymmetry and interest misalignment between the owner/ principal and the manager/agent, problems associated with managerial opportunism are important (Fama and Jensen, 1983; Jensen and Meckling, 1976). Berle and Means (1932) suggest that ownership concentration should have a positive effect on firm value because it alleviates the conflict of interests between owners and managers. Ownership concentration mitigates this conflict by bringing about greater alignment of incentives (if ownership is concentrated in the hands of managers themselves) or improved monitoring (if it is concentrated in the hands of outside shareholders. However, Demsetz (1983) argues that ownership concentration is the endogenous outcome of profit-maximizing decisions by current and potential shareholders, so that as a result, it should have no effect on firm value.

In a company with a large shareholder and a fringe of small shareholders (as modeled by Shleifer and Vishny, 1986), the classic owner-manager conflict (i.e. agency problem I) is mitigated due to the large shareholder’s greater incentives to monitor the manager. However, a second type of conflict appears (i.e. agency problem II) as the large shareholder may use its controlling position in the firm to extract private benefits at the expense of the small shareholders. The empirical corporate governance literature offers no unequivocal answer to the costs and benefits of concentrated ownership. Some scholars have found a positive association with corporate performance (LaPorta, López-de-Silanes, Shleifer, and Vishny, 2000), others a negative association (Loderer and Martin, 1997), and still others a curvilinear relationship (Morck, Shleifer, & Vishny, 1988), and Demsetz and Lehn (1985), Himmelberg et al. (1999), and Demsetz and Villalonga (2001) provide evidence in support of Demsetz’s arguments of no difference. Theoretically compelling arguments can be furnished in favor of each finding.

Although previous studies (e.g. Johnson et al. 2000; Laporta et al. 2002) have used cross-country analysis to demonstrate the importance of corporate governance characteristics in determining firm value, few researchers have investigated the importance of corporate governance for investors during periods of stock market turmoil. Having a wealthy, concentrated owner can help firms in times of crisis as such owners sometimes choose to transfer private resources into an ailing firm. This phenomenon, also known as “propping,” can help firms survive a temporary slump in performance, when owners choose “to invest private cash today in order to preserve their options to expropriate and to obtain a legitimate share of profits tomorrow” (Friedman, Johnson, and Mitton, 2003: 734). On the other hand, during periods of financial crisis, controlling shareholders have a larger incentive to transfers assets and profits out of better-performing firms towards their underperforming affiliates to “bail them out” (Gedajlovic and Shapiro, 2002; Granovetter, 2005).

A few studies have examined the link between corporate governance and firm value during periods of economic crisis. Using data for public companies in East Asia, Claessens et al. (2002) find that firm market value increases with the cash-flow ownership of largest shareholders, but drops when the control rights of largest shareholders exceed their cash-flow ownership. Similar results are found in Korea (Joh, 2003; Baek et al., 2004). Furthermore, Mitton (2002) shows a significant relationship between corporate governance mechanisms and stock price performance during the Asian crisis of 1997–1998 for a sample of firms from five East Asian countries. He argues that corporate governance becomes more critical in explaining cross-firm differences in performance during a financial crisis because of the increased incentive for expropriation of minority shareholders as well as the greater investor awareness of weaknesses in corporate governance in the region which could lead them to pull-out (Rajan and Zingales, 1998). Consistent with these arguments, Mitton (2002) finds that firms with higher outside ownership concentration experienced better stock price performance during the crisis.

H1a: Ownership concentration is positively related to stock price performance.

There is some evidence suggesting that that the effects of ownership structure on performance are nonmonotonic (Demsetz and Villalonga, 2001; Morck et al., 1988; McConnell & Servaes, 1990), and that they vary with the size of the concentrated owner’s stake. When a concentrated owner’s stake is relatively low, the owner has insufficient control to successfully engage in tunneling or other minority-disadvantaging strategies. Under these conditions, the concentrated owner’s most effective strategy for increasing his or her private wealth is to push the management of the firm for greater performance. Hence relatively low levels of concentrated ownership will have an overall positive influence on corporate performance. At higher levels of ownership, however, a point will be reached where the concentrated owner effectively controls the firm, while there is still a significant fraction of small investors to expropriate. Under these conditions tunneling is both feasible and lucrative, and the effect of concentrated ownership on corporate performance will cease to be positive. Since in many jurisdictions dominant shareholders can have control rights in excess of their ownership rights (La Porta et al., 1999), tunneling often becomes a real possibility. However, as the concentrated ownership stake increases further, tunneling becomes a less sensible strategy for increasing private wealth, as there will be fewer minority shareholders to expropriate. Tunneling would then simply result in a direct transfer of private wealth from one venture into another, which is unlikely to benefit the concentrated owner. As the best strategy for majority owners to increase their private wealth is to gear the firm for higher performance, very large ownership stakes are again likely to have a positive effect on firm performance. Previous research by Morck et al. (1988) has reported a nonmonotonic relationship between the degree of ownership concentration and firm profitability. In addition, Gedajlovic and Shapiro (1998) show evidence of a non-linear relationship between ownership concentration and profitability in US and German firms. Nevertheless, no relationship between concentration and profitability was found in the UK, France and Canada. Kaplan and Minton (1994) and Gorton and Schmidt (2000), on the other hand, found a linear relationship for a sample of Japanese and German firms respectively. For a sample of Spanish listed companies, De Miguel et al. (2004) find that ownership concentration has a nonlinear effect on firms’ value.

H1b: the relationship between ownership concentration and stock price performance follows a non-monotonic pattern.

2.1. Multiple blockholders

With the exception of Laeven and Levine (2007) and Maury and Pajuste (2005), most of the empirical studies focus little, if any, attention on the role of multiple blockholders in corporate governance. Blockholders are defined as shareholders who own at least 5 percent of a company’s common shares. Arguably, the neglect of the potential monitoring benefits of blockholders, beyond the largest controlling shareholder, reflects the assumption that the former represents a homogenous group of uninvolved stakeholders, with weak incentives and little power to engage in monitoring activities. However, La Porta et al. (1999), Claessens et al. (2000) and Faccio and Lang (2002) all document numerous instances of multiple large blockholders across the globe.

Only very recently have a few theoretical papers started to study how controlling groups are formed when there are multiple large shareholders (Zwiebel, 1995; Bennedsen and Wolfenzon, 2000) and which are the effects of multiple blockholders on monitoring (Pagano and Röell, 1998) and on the level of private benefit extraction (Bennedsen and Wolfenzon, 2000; Gomes and Novaes, 2001). Investors may associate the presence of multiple blockholders with efficient monitoring because large shareholders can bring valuable internal monitoring either by forming coalitions with large equity stakes or by competing for corporate control (e.g., Bennedsen and Wolfenzon, 2000; Bloch and Hege, 2001). Alternatively, multiple blockholders can present an opportunistic structure for coercive voting, where blockholders would find it mutually valuable to collude to extract divisible private benefits of control (e.g., Winton, 1993; Zwiebel, 1995; Kahn and Winton, 1998). These divergent perspectives imply that whether or not multiple blockholders serve a monitoring role in mitigating the agency problems that beset concentrated control remains an open question.

Empirical evidence validating the theoretical predictions is very limited. Volpin (2002) finds that the market value of Italian listed firms is higher for companies with a voting syndicate than for companies with a single large shareholder. Faccio et al. (2001) compare the dividend policies of listed companies across different countries and find that European companies pay higher dividends when they have multiple blockholders. Lehman and Weigand (2000) show that the presence of a second large shareholder improves the profitability of German listed companies.

H2a: The shareholdings by secondary blockholders is positively related to stock price performance.

H2b: The number of blockholders is positively related to stock price performance.

2.2. The identity of controlling owners

The classic owner-manager conflict is mitigated in the presence of large controlling shareholders. However, a second type of conflict appears as controlling owners may use their controlling position in the firm to extract private benefits at the expense of the small shareholders. If the large shareholder is a financial institution, such as a bank or an investment fund, the private benefits of control are diluted among several independent owners. If the large shareholder is an individual or a family, it has greater incentives for both expropriation and monitoring, which are likely to lead agency problem II to overshadow agency problem I (Villalonga and Amit, 2006). The different categories of concentrated owners may have different preferences and priorities with respect to corporate risk, stability, growth, and performance (Douma et al., 2006; Gedajlovic et al., 2005). We identify 4 different types of firms. First we distinguish between closely-held and widely held firms. In a second step, we distinguish between companies controlled by families (either direct or through non-financial companies) and companies controlled by financial institutions. Finally, for the companies controlled by families, we distinguish between firms controlled by founding families and firms controlled by non-founding families.

2.3. Firms controlled by families

Families may use their control over companies to extract private benefits of control at the expense of minority shareholders. The private benefit extraction may take different forms such as excessive compensation of family members or related-party transactions. In addition, families may be excessively interested in maintaining control over the company even in the presence of a potentially value increasing acquirer. When the family owns less than 100 percent of the shares of the company, it gives an excessive weight to private benefits of control over security benefits. Another type of cost of family ownership has to do with the family itself and the ties among its members. Family owners may have the incentive and power to assign key management positions to family members even when there are unqualified (Claessens et al., 2000). More generally, family priorities may conflict with the objectives of outside investors. Family control does not only come with cost, though. Families may have longer investment horizon with respect to other shareholders thereby avoiding managerial myopia. Because the company will be controlled by future generations of the family, family firms may be natural long term value maximizers.

Since family control can have both positive and negative properties, empirical evidence is of paramount importance for judging about its final effect and for orienting regulations that could hamper the persistence of family controlled firms. Recently, several papers have begun to analyze the performance of family firms. Denis and Denis (1994) study majority-owned firms, and find that, although most of them are characterized by family involvement, they do not exhibit specific inefficiency features. Morck et al. (2000), using Canadian data, provide evidence that family control deteriorates firm performance. Performance is measured using accounting data. They find that widely held firms have a superior performance with respect to family firms. Results are unaffected by having the founder, or one of the heirs, as a CEO. In addition they find no evidence of a longer horizon in decision making in family firms, as they invest less in R&D and have fewer employees than widely held firms. Faccio, Lang and Young (2001) report that family ownership in East Asia leads to severe conflicts with other claimants and hampers firm performance. Their results are supported by Baek et al. (2004) who find that Korean chaebol firms with concentrated ownership by controlling family shareholders experienced a larger drop in the value of their equity during the Korean crisis. As far as Western Europe is concerned, two cross-country studies by Barontini and Caprio (2006) and Maury (2006) using panel data observe that family firms may have a higher market valuation calculated as Tobin’s Q and a higher profitability under certain conditions. Barontini and Caprio find similarities to the study by Villalonga and Amit (2003) confirming that especially family firms with founder CEO perform better than other firms. However, compared to the evidence obtained for the United States, the performance of descendant owned firms appears to be different. If the descendant member of the family only assumes a non-executive position the firm still outperforms non-family firms, if he is CEO it performs as well as non-family firms and only if the family takes up no active role at all does it perform worse. Maury on the other hand, indicates that active management only enhances profitability but does not have a real impact on firm valuation.

Evidence on single European countries shows similar findings. Family firms outperform others depending on their characteristics. Both Favero et al. (2006) in their study of Italian listed family firms and Sraer and Thesmar (2007) on French listed family firms report that family firms outperform widely held companies and this independent of a founder, descendant or outsider managing the firm. However, Sraer and Thesmar (2007) additionally find that founders explain most of the outperformance and propose different reasons linked to labour force, wages and productivity to explain why the respective management type delivers superior performance. Favero et al. on the other side find evidence that market performance is not different for family firms which is due to the wrong measurement methods. When using a dynamic performance measurement approach they find similar positive results as for accounting measures. In a study on German listed firms Andres (2008) finds that family firms are not only more profitable than widely held companies but also than companies with other kinds of blockholders. In line with evidence found in Sraer and Thesmar (2007), he finds that performance increases in particular with a founding family member actively managing the company and founder-managed companies showing the strongest effect on performance.

H3a: Founding Family controlled firms are associated with higher stock price performance compared to widely held firms.

H3b: Non-Founding Family controlled firms are associated with lower stock price performance compared to widely held firms.

2.4. Firms controlled by financial institutions

Researchers have tested the monitoring effect of both institutional investors and block holders in a variety of settings. Early research studies partitioned large owners into financial and non-financial classifications and concluded that financial investors were better monitors than non-financial shareholders when measuring their effect on firm value (McConnell and Servaes, 1990) and risk taking (Wright et al., 1996). Financial institutions tend to have multiple ties with the firms in which they own shares. In addition to being shareholders, stable investors are often also creditors, debtors, buyers, suppliers, or alliance partners. Their equity stake primarily serves to cement an often complex set of non-shareholder relationships with the focal firm, and is often reciprocated to create cross-holdings (Roe, 1994). Lehmann and Weigand (2000) confirm the benefits of large shareholders in Germany, but only in the case of banks, since the presence of non-financial large owners negatively affects firm profitability.

H4: Financial controlled firms are associated with higher stock price performance compared to widely held firms.

3. Sample and data

Our sample is drawn from the population of Spanish firms listed on the Madrid Stock Exchange during January 2000- January 2008. Fundamental stakeholders in the Spanish corporations include banks and industrial firms, although the role of financial institutions is not as prevalent as in other countries such as Germany and Japan, and the main agency problem arises from controlling and minority shareholders, as occurs in most European countries. For this study, we consider all listed firms (both financial and non-financial) for which we could retrieve the stock price and ownership structure data. For the period January 2000- January 2008, we calculated, daily, the 10, 20 and 30 trading day performance of the Ibex-35 (the Spanish reference stock exchange market index). We then identified the ten largest jumps and plummets for each time frame, avoiding overlap of periods within each timeframe. Table 1 gives an overview of the starting dates for each time frame, as well as the IBEX-35 stock price performance. After identifying the periods of maximum stock market turmoil, we calculated the individual stock price performance for each listed company. Table 1 gives a summary of the number of companies considered in each period. Obviously, the sample of listed companies varies slightly across time, as some companies start listing on the stock market while others de-list during the period considered in this study.

The principal source of our ownership structure data is the database from the CNMV (Spanish Securities and Exchange Commission, the equivalent to the U.S. Securities and Exchange Commission). Under the Spanish Companies law, listed companies have to report to the CNMV the names and shareholdings of shareholders with blocks of shares of 5 percent or more and any holdings for those that seat in the board of directors. This requirement became active through the Spanish transposition of the European transparency directive (Council Directive of 12 December 1988 (88/627/EEC)). From these files of the CNMV, we analyzed a total of 24.786 communications to determine the ownership structure for any given date as well as all changes in the ownership structure between any two dates. To calculate the ownership measures, we consider both direct and indirect shareholding. The ownership data is determined for the first day of the turmoil period. We also calculate the changes in the ownership structure during the considered up or down market periods. In addition, for each firm we identified its founders using the company website, annual reports, and other public sources of information. Our access to this type of in depth ownership structure data goes until January 2008.

Spanish listed companies have a number of characteristics that make them particularly suited to our investigation, as it presents corporate governance characteristics similar to many other Continental European countries, but quite different from Anglo-American or Asian listed companies. Continental European countries in are typically categorized by concentrated ownership of firms, strong state intervention, and weak labour participation at company level (Rhodes and Van Apeldorn 1997; Aguilera and Jackson 2003). As noted in the World Bank’s 2008 “Doing Business Report”, investor protection in Spain is below the average achieved by member states of the OECD. The Investor Protection Index is a subcomponent of the World Bank’s 2008 Doing Business Indicators, and consists of three dimensions of investor protection: transparency of transactions (Extent of Disclosure Index), liability for self-dealing (Extent of Director Liability Index) and shareholders’ ability to sue officers and directors for misconduct (Ease of Shareholder Suits Index).

4. Model and variable specification

The aim of our paper is to examine the relationship between stock price performance and the ownership structure during periods of market turmoil. In order to evaluate the different aspects of the ownership structure on stock market performance, we regress the stock market returns on the different specifications of ownership and control for sector and size. The methodology adopted is similar to Mitton (2002) and Baek et al. (2004).

Stock Market Return_ij= f (Ownership structure Variables_ij, size_ij, industry_ij) (1)

i: up or down market, j: time frame: 10, 20 or 30 trading days

4.1. Dependent variable

To measure stock price performance, we use the individual stock market returns, calculated as the relative percent difference between the value of the share at the end of the period and the value at the beginning at the period. For the period, January 2000- January 2008, we identified the ten largest jumps and plummets for each time window and calculated the individual stock price performance for each listed company. Table 1 presents the exact dates of the first day of the considered up or down market periods. For a trading window of 10 days, the largest jump (15 percent) of the Ibex-35 occurred during the first days of February 2000, while the largest loss (-17.8 percent) was registered during September 2001. For the 20-day and 30-day window, the largest jumps were 19.7 percent and 22.0 percent respectively, while the largest drops were -22.7 percent and -23.0 percent respectively.

Stock exchange consolidation is at work since many years and has recently accelerated through competition for order flows, agreements and mergers, causing shocks to be transmitted from one stock market to another. Idier (2006) consider the DAX30, the CAC40, the FTSE100 and the NYSE indexes and documents stock exchanges convergence between European stock market indexes. To provide addition information of the nature of the identified short term shocks, we have calculated the performance of other leading stock market indices during the exact same periods. We present the comparative stock market movements (averages) in tables 2 and individual comparison for each period in Tables 3a-3f. Table 2 shows that the French, Italian, German, UK and US stock market indices display a very similar performance compared to the Ibex-35 over the selected periods. The Ibex-35 shows an average loss over a 10 day window, for the 10 periods we consider, of 13 percent, against a loss of 12 percent for the CAC-40, 11 percent for the MIB-30, 13 percent for the DAX-30, 10 percent for the FTSE-100 and an 8 percent loss for the S&P-500 market index, over the exact same periods. Both in up and down market the similarities hold, as well as for the different windows. This indicates that the extreme up and down market we consider in this study are not isolated from other stock markets and are, to a large extend, the results of worldwide events and stock market sentiment. In addition, tables 3a-f compares the performance of these stock market indices for each period of a plummeting or soaring Ibex-35. Again, for most of the periods, other important stock market indices suffer similar losses when the ibex-35 plummets and obtain similar gains when the ibex-35 soars. These descriptive results give additional validity for the dependent variable as it seems to capture not only large movements in the Spanish stock market but also global events which affect stock prices all over the world. Finally, it also provides support for the generalizability of this study.

Table 2. Comparative stock market movements of other stock market indices considering the same periods

4.2. Explanatory variables

For the ownership structure variables, we calculate several measures to test the hypotheses. To measure ownership concentration we use specifications in line with La Porta et al. (1999), Demsetz and Villalonga (2001), De Miguel et al. (2004) and Sánchez-ballesta and García-Meca (2007). Our first measure is the proportion of shares held by the largest shareholder (SH1). Alternatively we also use the proportion held by the three largest (SH1-3) and by the five largest (SH1-5) shareholders. Furthermore, we investigate whether the proportion of shares held by secondary shareholders (SH2-5), defined as the total shareholdings by the second to fifth largest shareholder helps to explain performance. In addition, we also look at the relationship between the total number of significant shareholders (N_SH) and stock price performance.

Finally, we investigate whether the type of controlling owner affects the share price during periods of soaring or plummeting financial markets. We identify 4 different types of firms: widely held firms, firms controlled by financial institutions (Cont_FI), firms controlled by founding families (Cont_FF) and firms controlled by non-founding families (Cont_NFF). We classify a firm as a widely held firm if the largest shareholder owns less than 20 percent of all shares. This threshold is in line with previous literature (Faccio et al., 2001, Anderson and Reeb, 2003, La Porta, 1999). For firms controlled by financial institutions, the largest shareholder is a financial institution and owns at least 20 percent of all shares. For firms controlled by founding families, the largest shareholder is the founder of the company or a relative of the founder and owns at least 20 percent. For firms controlled by non founding families, the largest shareholder owns at least 20 percent of all shares but is neither a member of the founding family nor a financial institution.

4.3. Control variables

To control for other factors that could affect stock price performance, we include firm size and industry into the regression models. We define firm size in terms of total market capitalization and define four categories: Ibex-35 firms, non-ibex firms with a market capitalization higher than €1000 million, firms with a with a market capitalization between €1000 million and €250 million, and firms with a market capitalization below €250 million. We define 12 industry categories using the Madrid Stock Exchange classification system. The size and industry categories are stable during the considered periods, eliminating the need to control for within period changes.

5. Results

The descriptive statistics of the variables of interest are presented in Table 4. The average loss, considering the entire sample over the selected periods is 7.10 percent for a 10 day window, 10.35 percent for a 20-day window and 10.00 percent for a 30-day window. The average gains are 4.66 percent for a 10 day window, 8.08 percent for a 20-day window and 10.57 percent for a 30-day window. The high degree of ownership concentration in Spain is reflected in the different measures of ownership concentration. The average ownership stake of the biggest shareholder is 31.23 percent, while the average proportion of shares held by the three largest shareholders is 42.81 percent and 46.39 percent for the five largest shareholders. These results are similar to the values reported for Spain in De Miguel et al. (2004). In addition, the descriptive statistics confirm the importance of secondary blockholders. The average number of blockholders in Spanish listed companies is 2.31 and an average shareholding by the second to fifth largest shareholder of 15.16 percent of the shares. To control for stock market movement driven by sales or purchases of stock by large shareholders we calculated the total change in shareholdings by the top five shareholders during each period. On average, large shareholders very rarely trade during short term periods of market turmoil. For the entire sample, the average change in shareholdings by the five largest shareholders is only -0.03 percent. Finally, the descriptive statistics show that roughly one third of the companies are widely held, which is in line with findings by De Miguel et al. (2004), while 17 percent is controlled by financial institutions, 30 percent by a member (or members) of the founding family and 19 percent by a non-founding family.

R ij: Stock price Return over a period of i trading days during a j (up/down) market

SH1: proportion of shares of the largest shareholder

SH1-3: proportion of shares of the 3 largest shareholders

SH1-5: proportion of shares of the 5 largest shareholders

SH2-5: SH1-5 – SH1

D_SH1-5: Change in total shareholdings by 5 largest shareholders during the period

N_SH: number of all significant shareholders

Widely: dummy variable taking value 1 if SH1<20%, 0 otherwise

Cont_FI: Firm controlled by a financial institution

Cont_FF: Firm controlled by the founding family

Cont_NFF: Firm controlled by a non-founding family

Table 4. Descriptive statistics

Table 5 provides the correlations of the different performance measures against the independent variables. The three measures of ownership concentration are positively related to stock price performance during down markets, and negatively related to performance during up markets. Furthermore, the presence of multiple blockholders seems especially beneficial during down markets, as we find that the number of significant shareholders and the total shareholders by the secondary shareholders correlate positively with the stock price performance measures during down markets. Finally, firms controlled by financial institutions tend to outperform other types of companies while firms controlled by non founding families tend to do significantly worse during down markets. The results from the table 5 seem to provide some support for the hypotheses 1a, 2a, 2b and 4. We also test for possible multicollinearity considering the independent and control variables. The Variance Inflation Factor (VIF) gives a mean value of 1.67 and a maximum value of 1.94, indicating that there are no multicollinearity problems.

*: significant at 10%; **: significant at 5%; ***: significant at 1%

R ij: Stock price Return over a period of i trading days during a j (up/down) market

SH1: proportion of shares of the largest shareholder

SH1-3: proportion of shares of the 3 largest shareholders

SH1-5: proportion of shares of the 5 largest shareholders

SH2-5: SH1-5 – SH1

D_SH1-5: Change in total shareholdings by 5 largest shareholders during the period

N_SH: number of all significant shareholders

Cont_FI: Firm controlled by a financial institution

Cont_FF: Firm controlled by the founding family

Cont_NFF: Firm controlled by a non-founding family

Table 5. Correlation matrix

Next, we consider the multiple regression models to test our hypotheses 1-4. Table 6 represent the regression analysis considering the relationship between the stock price performance and ownership concentration during extreme down markets. Models 1-3 show a consistent positive relationship between the shareholdings by the largest shareholder (SH1) and different specifications of stock price performance. The other two measures of ownership concentration, the shareholdings by the three largest and five largest shareholders, show a very similar image. These results provide strong support for hypothesis 1a. Furthermore, we observe that the changes in shareholdings during the considered periods are not significant. This is probably due to the infrequent trading of large shareholdings during periods of stock market turmoil. Finally, larger listed companies do not loose significantly more value during extreme down markets compared to smaller listed firms. Table 7 shows the results for the relationship between the stock price performance and ownership concentration during extreme up markets. The results for ownership concentration are the exact opposite during up markets. Our three measures of ownership concentration show a negative relationship with stock price performance over a 10, 20 or 30 days window. In addition, larger companies, especially those pertaining to the IBEX-35 perform better than smaller companies during extreme up market periods. In sum, the results from table 6 and 7 show that minority investors attach a significant positive value to ownership concentration during extreme down market periods, while ownership concentration is valued negatively during up market periods. This could be driven by the increased reliance on large shareholders by minority shareholders during down market periods. Alternatively, the value of firms with concentrated ownership may be perceived to be more stable, undergoing less severely external market shocks.

Dep var: Rij	H	R 10	R 20	R 30	R 10	R 20	R 30	R 10	R 20	R 30
		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Intercept		-6.043***	-8.230***	-8.717***	-6.823***	-9.239***	-10.018***	-7.158***	-9.626***	-10.622***
		(0.845)	(1.232)	(1.823)	(0.890)	(1.282)	(1.926)	(0.909)	(1.282)	(1.970)
SH1	+	0.033***	0.041***	0.036*
		(0.009)	(0.013)	(0.023)
SH1-3	+				0.040***	0.052***	0 .054***
					(0.009)	(0.013)	(0.023)
SH1-5	+							0.043***	0.056***	0.062***
								(0.009)	(0.013)	(0.023)
D_SH1-5	+	0.112	0.061	0.042	0.116	0.041	0.034	0.120	0.040	0.028
		(0.175)	(0.109)	(0.144)	(0.174)	(0.109)	(0.143)	(0.174)	(0.109)	(0.143)
IBEX35		-0.566	-1.112	-0.064	-0.506	-1.064	0.082	-0.493	-1.074	0.115
		(0.796)	(1.135)	(1.734)	(0.794)	(1.131)	(1.731)	(0.792)	(1.130)	(1.729)
Size_1000		1.084	0.638	1.398	1.001	0.414	1.116	0.830	0.264	0.906
		(0.743)	(1.059)	(1.627)	(0.743)	(1.060)	(1.627)	(0.744)	(1.063)	(1.634)
Size_250		-0.019	0.202	1.402	-0.096	0.033	1.212	-0.184	-0.079	1.052
		(0.684)	(0.979)	(1.476)	(0.683)	(0.979)	(1.473)	(0.683)	(0.981)	(1.475)
Industry		Included	Included	Included	Included	Included	Included	Included	Included	Included
N		983	998	968	983	998	968	983	998	968
R-squared		0.079	0.085	0.045	0.0844	0.090	0.049	0.0871	0.092	0.051
Prob>F		0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000

*: significant at 10%; **: significant at 5%; ***: significant at 1%

R ij: Stock price Return over a period of i trading days during a j (up/down) market

SH1: proportion of shares of the largest shareholder

SH1-3: proportion of shares of the 3 largest shareholders

SH1-5: proportion of shares of the 5 largest shareholders

D_SH1-5: Change in total shareholdings by 5 largest shareholders during the period, taking positives values if shareholdings increased

Ibex35, Size_1000, Size_250: Ibex35 firms, firms with market cap > €1000 million and firms with €1000 million >market cap> €250 million, respectively

Table 6. Regression analysis: Performance and ownership concentration during down markets

Dep var: Rij	H	R 10	R 20	R 30	R 10	R 20	R 30	R 10	R 20	R 30
		(10)	(11)	(12)	(13)	(14)	(15)	(16)	(17)	(18)
Intercept		5.150***	5.595***	12.959***	5.730***	6.632***	12.959***	5.786***	6.910***	13.864***
	+	(0.977)	(1.261)	(1.594)	(1.037)	(1.329)	(1.594)	(1.067)	(1.359)	(1.727)
SH1		-0.051***	-0.066***	-0.076***
		(0.011)	(0.013)	(0.023)
SH1-3	+				-0.045***	-0.067***	-0.069***
					(0.011)	(0.014)	(0.017)
SH1-5	+							-0.041***	-0.067***	-0.064***
								(0.011)	(0.014)	(0.017)
D_SH1-5	+	0.083	0.044	0.020	0.092	0.065	0.034	0.096	0.070	0.041
		(0.117)	(0.112)	(0.112)	(0.117)	(0.112)	(0.112)	(0.117)	(0.112)	(0.112)
IBEX35		2.647***	3.745***	-1.824	2.615***	3.771***	-1.741	2.658***	3.817***	-1.665
		(0.938)	(1.176)	(1.478)	(0. 942)	(1.176)	(1.481)	(0. 943)	(1.176)	(1.482)
Size_1000		1.028	2.924***	-2.376*	1.039	3.077***	-2.282	1.081	3.193***	-2.219
		(0.890)	(1.101)	(1.387)	(0.895)	(1.104)	(1.394)	(0.901)	(1.109)	(1.400)
Size_250		-0.040	0.841	-3.192**	0.060	0.931	-3.133**	0.1060	1.022	-3.076**
		(0.801)	(1.014)	(1.274)	(0.804)	(1.016)	(1.278)	(0.808)	(1.020)	(1.284)
Industry		Included	Included	Included	Included	Included	Included	Included	Included	Included
N		927	998	1020	927	998	1020	927	998	1020
R-squared		0.143	0.126	0.064	0.138	0.126	0.061	0.136	0.125	0.059
Prob>F		0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000

*: significant at 10%; **: significant at 5%; ***: significant at 1%

R ij: Stock price Return over a period of i trading days during a j (up/down) market

SH1: proportion of shares of the largest shareholder

SH1-3: proportion of shares of the 3 largest shareholders

SH1-5: proportion of shares of the 5 largest shareholders

D_SH1-5: Change in total shareholdings by 5 largest shareholders during the period, taking positives values if shareholdings increased

Ibex35, Size_1000, Size_250: Ibex35 firms, firms with market cap > €1000 million and firms with €1000 million >market cap> €250 million, respectively

Table 7. Regression analysis: Performance and ownership concentration during up markets

Table 8 looks at the hypothesised non-monotonic relationship between ownership concentration and stock price performance, as well as the importance of secondary blockholders during down market periods. Models 19-21 introduce the squared term of the total shareholdings by the largest shareholder, into models 1-3. The results do not provide support for a non-linear relationship between ownership concentration and stock price performance. We therefore conclude, considering models 1-3 and 19-21 that the relationship between performance and ownership concentration is linear and positive during down market periods. Furthermore, models 22-27 consider the importance of secondary blockholders. We consider therefore the total number of blockholders as well as the shareholdings by the second to fifth largest shareholder. Models 22-24 show a significant positive relationship between the total number of blockholders and performance, confirming hypothesis 2a. In addition, the shareholdings by the secondary blockholders are also positively related to performance, confirming hypothesis 2b.

Table 9 looks at the hypothesised non-monotonic relationship between ownership concentration and stock price performance, as well as the importance of secondary blockholders during up market periods. Similar to the results for the down market, models 28-30 do not provide support for a non-linear relationship between ownership concentration and stock price performance. Moreover, models 31-33 show no significant relationship between the total number of blockholders and performance. In addition, no significant relationship is found between the shareholdings by the secondary blockholders and performance in models 34-36. Overall, from tables 8 and 9, we conclude that the relationship between performance and ownership concentration is linear, and positive during down market periods and negative during up markets. In addition, the presence of multiple blockholders is clearly beneficial during down markets, but shows no significant relationship with performance during up markets. This could be driven by the increased reliance on multiple blockholders by minority shareholders during down market periods to provide effective monitoring.

Dep var: Rij	H	R 10	R 20	R 30	R 10	R 20	R 30	R 10	R 20	R 30
		(19)	(20)	(21)	(22)	(23)	(24)	(25)	(26)	(27)
Intercept		-5.830***	-7.817***	-8.113***	-8.066***	-10.274***	-12.488***	-7.447***	-10.091***	-11.787***
		(0.974)	(1.395)	(2.119)	(0.989)	(1.440)	(2.184)	(0.950)	(1.372	(2.078)
SH1	+	0.0181	0.012	-0.004	0.041***	0.050***	0.053***	0.040***	0.052***	0.053***
		(0.034)	(0.047)	(0.074)	(0.009)	(0.013)	(0.021)	(0.009)	(0.013)	(0.021)
SH1_SQ	–	0.0002	0.0003	0.0004
		(0.0003)	(0.0005)	(0.0008)
N_SH	+				0.636***	0.642***	1.101***
					(0.165)	(0.236)	(0.354)
SH2-5	+							0.060***	0.083***	0.123***
								(0.019)	(0.027)	(0.040)
D_SH1-5	+	0.111	0.063	0.042	0.110	0.058	0.018	0.129	0.034	0.010
		(0.175)	(0.109)	(0.144)	(0.174)	(0.109)	(0.143)	(0.174)	(0.109)	(0.141)
IBEX35		-0.548	-1.074	-0.045	-0.632	-1.246	-0.218	-0.515	-1.124	0.071
		(0.796)	(1.137)	(1.734)	(0.791)	(1.133)	(1.722)	(0.792)	(1.130)	(1.727)
Size_1000		1.167	0.805	1.618	0.794	0.313	0.872	0.794	0.194	0.787
		(0.747)	(1.090)	(1.673)	(0.741)	(1.062)	(1.627)	(0.745)	(1.065)	(1.630)
Size_250		-0.034	0.173	1.348	-0.286	-0.099	0.998	-0.199	-0.114	1.047
		(0.686)	(0.981)	(1.476)	(0.683)	(0.983)	(1.475)	(0.683)	(0.981)	(1.474)
Industry		Included	Included	Included	Included	Included	Included	Included	Included	Included
N		983	998	968	983	998	968	983	998	968
R-squared		0.079	0.085	0.045	0.093	0.092	0.054	0.0882	0.093	0.054
Prob>F		0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000

*: significant at 10%; **: significant at 5%; ***: significant at 1%

R ij: Stock price Return over a period of i trading days during a j (up/down) market

SH1, SH1_SQ: proportion of shares of the largest shareholder and squared term of SH1

N_SH , SH2-5: Shareholdings by secondary blockholders and number of all significant shareholders, repectively

D_SH1-5: Change in total shareholdings by 5 largest shareholders during the period

Ibex35, Size_1000, Size_250: Ibex35 firms, firms with market cap > €1000 million and firms with €1000 million >market cap> €250 million, respectively

Table 8. Regression analysis: Performance, concentration and secondary blockholders during down markets

Dep var: Rij	H	R 10	R 20	R 30	R 10	R 20	R 30	R 10	R 20	R 30
		(28)	(29)	(30)	(31)	(32)	(33)	(34)	(35)	(36)
Intercept		5.011***	5.509***	10.629***	5.356***	7.126***	13.049***	4.844***	6.315***	12.520***
		(1.126)	(1.261)	(1.799)	(1.197)	(1.329)	(1.914)	(1.114)	(1.425)	(1.799)
SH1	+	-0.041	-0.060	.0891	-0.052***	-0.066***	-0.078***	-0.049***	-0.071***	-0.074***
		(0.040)	(0.013)	(0.062)	(0.011)	(0.013)	(0.017)	(0.012)	(0.014)	(0.017)
SH1_SQ	–	-0.0001	-0.00006	-0.002***
		(0.0004)	(0.0005)	(0.0006)
N_SH	+				-0.059	-0.472*	-0.027
					(0.200)	(0.251)	(0.327)
SH2-5	+							0.013	-0.031	0.019
								(0.022)	(0.029)	(0.036)
D_SH1-5	+	0.083	0.044	0.017	0.086	0.058	0.027	0.078	0.056	0.014
		(0.117)	(0.112)	(0.112)	(0.117)	(0.112)	(0.113)	(0.117)	(0.112)	(0.113)
IBEX35		2.643***	3.746***	-2.017	2.643***	3.834***	-1.821	2.673***	3.743***	-1.809
		(0.938)	(1.179)	(1.475)	(0. 932)	(1.174)	(1.479)	(0.939)	(1.176)	(1.478)
Size_1000		0.978	2.888***	-3.186**	1.064	3.161***	-2.363	0.957	3.082***	-2.475*
		(0.912)	(1.102)	(1.427)	(0.935)	(1.106)	(1.396)	(0.899)	(1.111)	(1.407)
Size_250		0.048	0.845	-3.113**	0.066	1.085	-3.179**	-0.012	0.964	-3.269**
		(0.802)	(1.015)	(1.270)	(0.806)	(1.020)	(1.275)	(0.806)	(1.020)	(1.280)
Industry		Included	Included	Included	Included	Included	Included	Included	Included	Included
N		927	998	1020	927	998	1020	927	998	1020
R-squared		0.143	0.126	0.072	0.143	0.129	0.065	0.144	0.127	0.065
Prob>F		0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000

*: significant at 10%; **: significant at 5%; ***: significant at 1%

R ij: Stock price Return over a period of i trading days during a j (up/down) market

SH1, SH1_SQ: proportion of shares of the largest shareholder and squared term of SH1

N_SH , SH2-5: Shareholdings by secondary blockholders and number of all significant shareholders, repectively

D_SH1-5: Change in total shareholdings by 5 largest shareholders during the period

Ibex35, Size_1000, Size_250: Ibex35 firms, firms with market cap > €1000 million and firms with €1000 million >market cap> €250 million, respectively

Table 9. Performance, concentration and secondary blockholders during up markets

*: significant at 10%; **: significant at 5%; ***: significant at 1%

R ij: Stock price Return over a period of i trading days during a j (up/down) market

Cont_FI: Firm controlled by a financial institution

Cont_FF: Firm controlled by the founding family

Cont_NFF: Firm controlled by a non-founding family

D_SH1-5: Change in total shareholdings by 5 largest shareholders during the period

Ibex35, Size_1000, Size_250: Ibex35 firms, firms with market cap > €1000 million and firms with €1000 million >market cap> €250 million, respectively

Table 10. Performance, type of ownership during down/up markets

The results for the hypotheses related to the relationship between performance and the type of ownership are presented in table 10. The models 37-39 focus on down market periods and show a significant positive performance for firms controlled by financial institutions (compared to widely held firms) and a significant negative performance for firms controlled by non founding families (compared to widely held firms). In contrast, the models 40-42 focus on up market periods and show a negative (but not significant) performance for firms controlled by financial institutions and a positive (but not significant) performance for firms controlled by non founding families.

In summary, the results show the importance of ownership concentration, the presence of multiple blockholders and type of ownership to explain stock market performance. In addition, the results for extreme down markets are fundamentally different from the up market results. While ownership concentration is valued positively during down market periods, it is valued negatively during up market periods. Furthermore, the presence of multiple blockholders only influences the stock price during down market periods. Finally, firms controlled by financial institutions loose less value during down markets, while firms controlled by non-founding families loose more value compared to widely held firms during down market periods. No significant relationship is found during up market periods. It is important to notice that the results are robust for different specification of stock market return, i.e. for 10, 20 or 30 trading days. In addition, although there is some overlap, each window has some specific periods not covered by the other windows, which reinforces the robustness of the findings. Finally, the results are also robust for different specifications for ownership concentration or presence of multiple blockholders. In addition, focusing on a Continental European stock market allows us to sidestep the endogeneity critique of the ownership literature, for at least two reasons. First, investors can neither acquire nor divest controlling blocks without incurring significant costs, and must therefore seek to maximize the performance of corporations given the block of shares they own in them (Stiglitz, 1994). Ownership can therefore be treated as an exogenous variable. Second, using a given set of measures for ownership structure immediately before the crisis to explain changes in the stock prices, while controlling for changes during the considered period, largely eliminates any spurious causality.

6. Conclusions and Limitations

Shareholder structures are quite diverse across countries, with dispersed ownership being much more frequent in US and UK listed firms, compared to Continental Europe, where controlled ownership is prevalent. The differences in ownership structure have two obvious consequences for corporate governance: on the one hand, dominant shareholders have both the incentive and the power to discipline management; on the other hand, concentrated ownership can create conditions for a new problem because the interests of controlling and minority shareholders are not aligned. Since ownership control can have both positive and negative properties, empirical evidence is of paramount importance for judging about its final effect and for orienting regulations that could hamper the persistence of large controlling shareholders. In this study, we investigate the relationship between stock market performance and ownership structure during plummeting and soaring financial markets in a Continental-European setting.

The results show the importance of ownership concentration, the presence of multiple blockholders and type of ownership to explain stock market performance. In addition, the results for extreme down markets are fundamentally different from the up market results. While ownership concentration is valued positively during down market periods, it is valued negatively during up market periods. Furthermore, the presence of multiple blockholders only influences the stock price during down market periods. Finally, firms controlled by financial institutions loose less value during down markets, while firms controlled by non-founding families loose more value compared to widely held firms during down market periods. No significant relationship is found during up market periods. It is important to notice that the results are robust for different specification of stock market return, i.e. for 10, 20 or 30 trading days. In addition, although there is some overlap, each window has some specific periods not covered by the other windows, which reinforces the robustness of the findings. Finally, the results are also robust for different specifications for ownership concentration or presence of multiple blockholders. These results support the idea that minority investors rely on large shareholders during down market periods to monitor management or pursue maximum firm value. This idea is further supported by positive relationship between multiple blockholders and stock price performance, only during down market periods. Alternatively, the value of firms with concentrated ownership may be perceived to be more stable, undergoing less severely the external market shocks. The results add to the existing literature on the relationship between ownership structure and performance, highlighting the importance of ownership concentration, secondary blockholders as well as the type of the controlling owner from a minority shareholder’s perspective.

We recognize that it would be insightful to focus on a multiple country. However, the country investigated shares the same institutional framework with most Continental European countries and shows many similarities concerning the ownership structure of firms. Furthermore, the precise ownership data we obtained from for our setting are difficult to obtain for other settings. However, it would be interesting to perform a similar analysis in settings where different types of owners play more or less prominent role. For example, in a US setting it would be interesting to see the relevance of institutional investors during periods of market turmoil, while in Japan or Germany, it would be interesting to see whether the impact of financial institution is similar.Finally, while our analysis employs pooled OLS regressions, the use of survival analysis or panel data would be alternative methodological approaches.

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[FULL] Article 1, Volume 1 Issue 2

The international transfer of technology: Examples from the development of accounting

Author

Christopher Nobes – (Royal Holloway, University of London)

DOI:

10.26595/eamr.2014.1.2.1

Abstract

This paper examines the international transfer of accounting technology, using seven examples in chronological order. These include double-entry bookkeeping, the true and fair view requirement, and the second income statement. Some overarching features of these transfers are noted. Inventions respond to commercial need, particularly increasing complexity. They therefore spread slowly if complexity spreads slowly. Common culture and language can speed up transfers but are not the major drivers. Political and legal intervention spreads technology, but sometimes inappropriately.

Keywords

1. Introduction

This paper considers the international transfer of accounting technology, starting with the invention and spread of double entry bookkeeping, and finishing with some developments of the twenty-first century. Table 1 shows the topics to be examined. Some overarching features of these transfers are noted. Conclusions are reached about the international transfer of accounting technology.

Debits and credits
Double entry
Published, audited financial statements
Consolidation
The equity method
The “true and fair view”
The second income statement: “other comprehensive income”

Table 1: Accounting inventions: in chronological order

The provenance of this paper (a lecture at the Reial Acadèmia de Doctors in Barcelona) explains the use of some informal language, why there is less referencing than in some academic papers, and why there are many references to Spain.

Parker (1989) traces the spread across time and across countries of professional accountancy and other accounting inventions. He concludes that diffusion is most successful when both exporters and importers of accounting technology are actively involved. Sometimes, diffusion goes too far, when a country copies unsuitable or unnecessarily expensive accounting techniques. Briston (1978) suggests that this occurred in developing countries, and Nobes (1998) mentions some African countries which have no stock markets but nevertheless use British-style investor-oriented accounting.

2. The invention and spread of double entry

2.1. Before double entry

Double-entry bookkeeping (DEB) gradually evolved, almost certainly in thirteenth century Italy, from more primitive forms of record keeping which already included the concepts of debit and credit. It is not surprising that Spanish words for accounting come from Italian but the same is also true for English words, as the examples in Table 2 show. That is, not only ideas but also the words for them are involved in international transfer.

Account	Debit, Debtor
Amortise	Discount
Audit	Dividend
Balance	Folio
Bank	Fund
Capital/ise	Interest
Cash	Liquidity
Contract	Pension
Cost	Reserve
Credit, Creditor	Value

Table 2: Examples of English accounting words of Italian origin

Imagine thirteenth century Florence. Apart from banking, a major type of business was clothing, as it is now. Family businesses (like Prada, Gucci, Versace or Dolce & Gabbana) turned raw ingredients (e.g. wool from England) into expensive luxury clothing. Given that accounting uses up time and expensive paper, a business keeps its accounting to a minimum. There is no need to record the payment of wages: we either have no staff or we only pay wages on Friday. There is no need to record cash or inventory: we can look in the cash box or in the inventory cupboard. We do not need to calculate profit because there is no-one to share it with and there is no tax on profits. However, what the business must record is debts by customers and debts to suppliers. Without such records, financial chaos would soon result.

At the time (e.g. early 12th century), there was no “zero” and no negative numbers in Christian Europe. Accounting worked with Roman numerals and an abacus. There were no crossings out. In an account for a customer, increasing debt (caused by sales) is shown on the left (as “debit”, meaning “he owes”), whereas receipts from the customer are shown on the right (as “credit”, meaning “he trusts”). In an account for a supplier, amounts owed are on the right, whereas payments to the supplier on the left.

We can speculate that the credits were put on the right because they were “good”: a supplier trusting us. There is a long history of the right being good, and the left being bad. This can be illustrated with paintings of “The Last Supper” in which Judas is on Christ’s left, or paintings of “The Last Judgement” in which the good go up to heaven on Christ’s right.

2.2. The invention of DEB

As commerce became more complex (e.g. taking on staff or taking on partners), better record keeping became useful. For example, if an employee looks after cash, it becomes necessary to know how much cash there should be not just how much there is. This can be done by treating the cashier as a customer. The receipt of cash from a real customer is then recorded as: debit cashier, credit customer. If a supplier is paid, the entry is debit supplier, credit cashier. At any time, it is then possible to calculate the balances of amounts owed by customers and to suppliers, and the amount of cash that is owed to the business by the cashier.

Eventually, all transactions can be recorded with double entries. Then the total of the debits should equal the total of the credits. At the end of a period, the balances on some of the accounts (e.g. sales and wages) are used to calculate profit. The remaining balances (e.g. cash and amounts owed) could be listed on a balance sheet, to show the financial position of the business at the period end.

The earliest DEB accounting records that we still have date from 1299 in Provence (Lee, 1977) and 1305 in London (Nobes, 1982). In both cases, the records were kept by Italian merchants. Furthermore, a study of thirteenth century Italian records shows that elements of DEB were gradually evolving before 1299 (Lee, 1973). This is all strong evidence that DEB was invented in northern Italy before 1300. The invention was probably driven by increasing complexity. Accounting developments tend to happen in innovative commercial nations. In this case, the Italians were ahead in such matters as: trading on credit, partnerships, foreign branches, multiple currencies.

Can we identify a particular inventor of DEB? Wikipedia, that great fount of student wisdom, begins its entry on Pacioli by stating that “in fact, he is the Father of double entry Book-keeping system” [capitals and doubtful grammar in the original][1]. Given that we have DEB records from 1299 whereas Pacioli was born in the 1440s, this is clearly inaccurate. Pacioli was a Franciscan friar and professor of mathematics. He had learned commercial arithmetic while being a tutor to a merchant’s children in Venice (Taylor, 1980). His contribution was to describe DEB in a part of a major work on mathematics (Summa de Arithmetica, Geometria, Proportioni et Proportionalita) which was published in Venice in 1494. Many papers have been written on the accounting part of the book, several recently (e.g. Sangster et al., 2008).

Pacioli’s treatise on bookkeeping led to translations or versions in several languages (e.g. in Flanders and in England in 1543). In Spain, an apparently original work on DEB by Bartolomé Salvador de Solóranzo appeared in 1590 (Esteve, 1989).

Spread of practice

Partly as a result of the availability of publications explaining DEB, the method spread throughout Europe, but slowly. Table 3 gives examples. Spain led the world in terms of requirements for merchants to use DEB, in the Pragmáticas of Emperor Carlos I in 1549.

1290s: Italian merchants

1340s: City of Genoa

1500s: slow spread via Antwerp to merchants around Europe

1549: Pragmáticas requiring merchants to use double entry

1746: City of Bristol

1826: the UK Exchequer

Table 3: Examples of the introduction of double entry

An example of the extraordinarily slow spread of practice is that the administrators of the City of Genoa[2] in Italy used DEB in the 1340s but it took until 1786 for the administrators of Bristol in England to adopt DEB. This was despite the fact that boats frequently travelled between these two major ports every year. It would not be a lack of knowledge or a language difficulty which caused the slow spread of practices. Presumably, Bristol did not need the complication of DEB.

In 1826, the UK state’s financial administration (the Exchequer) eventually adopted DEB. It then burnt the old tally sticks which had previously been used for accounting. This led to the destruction by fire of the Houses of Parliament in 1834, now replaced by the famous Victorian gothic buildings.

3. Published, audited financial statements

There were no general requirements in Spain for companies to publish audited financial statements until 1989 when the EU Fourth Directive was implemented (Gonzalo and Gallizo, 1992, p.74). By contrast, in the UK, publication has been required since 1844, and audit since 1900. This remarkable difference is obviously not caused because Spain did not know about the inventions but because the inventions were not needed in Spain.

The industrial revolution appeared first in the UK, and it led to the need for large amounts of capital which was provided by a large number of private investors. The model for this had been tried out earlier by the East India Company from 1600, and somewhat similarly in Amsterdam. The ability to set up companies by simple registration was allowed by the Companies Act 1844, and then limited liability was allowed by the 1855 Act. This led to many companies having widespread ownership. Publication and audit help to protect the outside (non-manager) owners from bad managers.

Spain (like France, Germany and Italy) took a different route. Industrialisation came later, and many companies were owned or financed by the state, by banks or by families. There was therefore no need for published statements, which meant that taxation became a major purpose of accounting. By contrast, in the UK (and under IFRS) taxation is specifically ruled out as the prime purpose of financial reporting, and tax calculations use different rules.

Scandals and catastrophes affect the arrival of new ideas and rules. The failure of the City of Glasgow Bank led to the world’s first audit requirements (in the Companies Act 1879, for banks in the UK). The Wall Street Crash led to the world’s oldest and toughest regulatory agency for accounting and auditing: the Securities and Exchange Commission (SEC), founded in Washington DC in 1934. The SEC immediately introduced the requirement for audited published financial statements for listed companies.

4. Consolidation

The first set of consolidated statements in the world was that published by US Steel in 1902. There are various explanations for why the US should have led developments in this field (Nobes and Parker, 2012, pp. 382/3). The idea gradually spread into the practice of some large companies elsewhere: UK (1910), Australia (1931), Germany (1930s), France (1960s). Compulsory consolidation followed later (e.g. US requirements of 1934 and German law of 1965).

The EU caught up with this by passing the Seventh Directive on company law in 1983, which led to Spanish law in 1989 and Spanish practice from 1991. This gradual spread around the world over nearly a century is summarised in Table 4. That it took so long for consolidation to spread from the US to Spain is related to the purposes of accounting and to the much greater prevalence of group structures in the US.

US: 1902 [1934 law]

UK: 1910 [1947 law]

Australia: 1931 [1938 law]

Germany: 1930s [1965 law]

France: 1960s [1986 law]

EU Directive: [1983 law]

Spain: [1991 law]

Table 4: The arrival of consolidation in practice and law

5. The equity method

The equity method (puesta en equivalencia) has a long history of different uses in different countries (Nobes, 2002). It brings entities into the financial statements of an investor, in a single line, as the share of net assets of an investee. The method was used in the UK in the 1910s to 1930s before consolidation; in unconsolidated statements in the US and the Netherlands; in the US and the UK for unconsolidated subsidiaries (e.g. in the 1980s); and now throughout the world to include associates and joint ventures in consolidated statements. For this latter purpose, it arrived in requirements as follows: France (1968), UK and US (1971), IFRS (1976), EU Seventh Directive (1983), Spain (1991) and Australia (1998).

However, no-one is really sure what the equity method is trying to do: is it valuation or consolidation (EFRAG, 2014)? It might be better to abandon it (Milburn and Chant, 1999). As noted in the introduction, it is perfectly possible for ideas to travel to inappropriate places or for bad ideas to travel all round the world.

6. The true and fair view

UK law contains the idea (since 1947) that it is even more important for a company’s financial statements to give a true and fair view than for them to comply with the detail of regulations. Therefore, the law foresees occasions when a company must depart from the details of the law in order to give a true and fair view (TFV).

This idea spread all round the British commonwealth, e.g. arriving in Australian law in 1955. However, it did not spread to Canada or the USA. After a battle in Brussels in the 1970s (see Nobes, 1983), the TFV requirement and its overriding nature were included in the EU’s Fourth Directive on company law, and arrived in Spanish law in 1989 as “la imagen fiel”. Incidentally, this is a problematic translation. The “la” implies that there can be only one TFV, which is not the British concept. Further, the “fiel” is bearing the weight of two quite different English adjectives: “true” and “fair”.

International Financial Reporting Standards (IFRS) did not deal with the TFV issue in Standards until a revision of IAS 1 in 1997, in which TFV is expressed as “fair presentation”. There was a major argument against the idea that it should be overriding (Camfferman and Zeff, 2007, p.390-392). Nevertheless, IAS 1 (para. 19) contains that requirement “if the relevant regulatory framework requires, or otherwise does not prohibit, such a departure”. Thus, it applies in Spanish law, but if IFRS were ever adopted in the USA, the override would then not apply there.

7. The second and income statement

Spanish law, based on the EU’s Fourth Directive, requires a company to present a cuenta de pérdidas y ganancias. For some individual companies, this statement includes all a company’s gains and losses of the year. However, for more complex entities, some gains and losses are excluded. For example, suppose that a group of companies based in Barcelona owns a subsidiary company in the United States. The assets (e.g. the American land and buildings) of the subsidiary are included in its own balance sheet (in dollars) and they are also in the balance sheet of the group (which is in euros). Suppose that the dollar rises in value against the euro. Accountants would increase the measure of the properties in the group’s euro balance sheet. DEB requires that this is also recorded as some sort of gain. However, the properties have not been sold, there may be no intention to sell them, cash has not been received, and the dollar might fall again next month. Perhaps[3] for such reasons, the a gain is not recorded as “profit or loss”.

In the UK in 1992, a new statement was invented to record this type of gain or loss and some other types (e.g. revaluing buildings): “the statement of total recognised gains and losses” (STRGL)[4]. This invention was in response to a financial crisis in which a company called Polly Peck had not recorded the above type of currency losses in any statement but had recorded another related type[5] of currency gain as profit or loss. The STRGL was also used in UK practice until 2014[6] to record other items, including revaluing fixed assets and actuarial gains and losses.

Accounting practices differ internationally, and there were more things to put into the STRGL than there would be in most other countries. For example, in the USA, it is not permitted to revalue tangible or intangible fixed assets. Nevertheless, in 1997, both US generally accepted accounting principles (GAAP) and IFRS followed the UK precedent, by requiring a “Statement of changes in equity”. However, in IFRS, entities were given a choice: present either (i) a statement like a UK STRGL (called in IFRS from 2005 a “Statement of recognised income and expense”)[7], or (ii) a statement containing all changes in equity, i.e. not just all income/expense but also new share capital, dividend payments, policy changes and corrections of errors. In 2007, IFRS was amended to require both statements: (i) “comprehensive income” (separately showing other comprehensive income, OCI)[8] and (ii) changes in equity. From 2012 and 2015, respectively, both statements are also required in US GAAP and UK GAAP. So, reporters under any of these three GAAPs now show five financial statements: balance sheet, profit or loss, comprehensive income, changes in equity, and cash flow statement. The GAAPs allow companies to combine the profit/loss with OCI statements, but this is very rare in practice.

In Spain, the 2007 revision of the plan general de contabilidad reacted to the US/IFRS changes of 1997 by requiring[9] a statement of changes in equity (estado de cambios en el patrimonio neto). However, this statement is a compromise between the two IFRS approaches of 1997 because it begins with a separate statement of “ingresos y gastos reconocidos” (a translation of the IFRS term “recognised income and expense”). Thus Spain became the first European country to require a full statement of changes in equity and the first continental European country to require the disclosure of OCI in a financial statement.

Neither of the two new statements have arrived in EU Directives, despite a revision of the Directives in 2013. Therefore the statements are not yet required in most EU countries. The position changes nearly every year, but Table 5 summarises it in 2011, as an example. The exact names of the statements have also changed over time.

	US	Spain	UK	IFRS	EU Directives
Balance sheet	ü	ü	ü	ü	ü
Profit or loss*	ü	ü	ü	ü	ü
Comprehensive income, including OCI*	?	x	ü	ü	X
Changes in equity, including OCI	?	ü	x	ü	x
Cash flow	ü	ü	ü	ü	x

Table 5: Statements required in 2011

*= US GAAP and IFRS allow profit/loss and OCI to be shown as a single statement but that is rare.

?= Entities had to present one of these two statements.

8. Conclusions

The examples in this paper show that accounting technology tends to move very slowly from one country to another. This is generally not caused by lack of information or by language difficulties. The inventions originally happen in response to new needs, which result from new complexity, including new ways of doing business or financing it.

Complex business requires complex accounting. The geographical locations of advances in accounting are therefore related to thriving economies. Thirteenth century Italy saw the invention of double entry. Sixteenth century Spain saw its imposition on merchants. Nineteenth century Britain saw the development of published audited accounting. Twentieth century America invented consolidation.

Accounting inventions travel with commerce and colonisation. They move fastest to places with a shared language and culture. So, inventions move quickly from the UK to Australia, but more slowly from Italy to the UK or from the UK to Spain. However, there is little evidence that the spread of inventions is seriously held up by ignorance or language. It is more likely that some inventions are unnecessarily complicated for some companies or economies to need.

In the late twentieth century, many accounting inventions were spread by international standardisation. For example, this brought new techniques to Spain. Under these circumstances, some of the spread of technology might be inappropriate because it might not correspond to commercial needs. The trigger for new laws which impose new techniques is often some form of crisis. More recently, EU harmonisation has brought the compulsory transfer of ideas.

When appropriate accounting serves commerce, the effects can be highly positive. There has been a debate about whether DEB was a vital spur to the development of capitalism or merely oiled its wheels (e.g. Sombart, 1924; Yamey, 1964; Edwards, 1989, p.63; Bryer, 1993). Much more recently, researchers have detected many improvements, for large groups, associated with the adoption of IFRS, e.g. reduced cost of capital, greater accuracy of analysts’ forecasts, and reduced bid-ask spreads (see, for example, ICAEW, 2014).

[1] http://en.wikipedia.org/wiki/Luca_Pacioli, accessed 29.5.2014

[2] That is, Genova in Italian or Génova in Spanish.

[3] There is no principle in IFRS which explains why some income and expenses are presented as ‘other comprehensive income’.

[4] Required by Financial Reporting Standard No. 3.

[5] Suppose that the above Spanish group had borrowed dollars to buy a US subsidiary, and then the dollar fell in value. Under certain conditions, the gain (caused by the reduction in the burden of the debt in euro terms) would be ‘profit’ but the loss on the US assets would not be.

[6] When UK accounting standards were replaced by a UK version of the IFRS for SMEs.

[7] This title was imposed by an amendment to IAS 19 in 2004 (para. 93B).

[8] IAS 1 allows an entity to show OCI after profit or loss as part of a statement of comprehensive income. However, in practice, nearly all companies show two income statements.

[9] Tercera parte, 1, 8a.

References

Briston, R.J. (1978). The evolution of accounting in developing countries. International Journal of Accounting, Fall.

Bryer, R.A. (1993). The late nineteenth-century revolution in financial reporting: accounting for the rise of investor or managerial capitalism. Accounting, Organizations and Society, 18 (7/8), 649-690.

Camfferman, K. and Zeff, S. (2007). Financial Reporting and Global Capital Markets. Oxford: Oxford University Press.

Edwards, J.R. (1989). A History of Financial Accounting. London: Routledge.

EFRAG, (2014). The Equity Method: A Measurement Basis or One-line Consolidation?. Brussels: European Financial Reporting Advisory Group.

Esteve, E.H. (1989). The life of Bartolomé Salvador de Solóranzo: some further evidence. Accounting Historians Journal, 16(1), 87-99.

Gonzalo, J.A. and Gallizo, J.L. (1992). European Financial Reporting: Spain. London: Routledge, 74.

ICAEW, (2014). The Effects of Mandatory IFRS Adoption in the EU: A Review of Empirical Research. London: Institute of Chartered Accountants in England and Wales.

Lee, G.A. (1973). The Florentine bank ledger fragments of 1211: some new insights. Journal of Accounting Research, 11(1), 47-61.

Lee, G.A. (1977). The coming of age of double entry: the Giovanni Farolfi ledger of 1299-1300. Accounting Historians Journal, 4(2), 79-95.

Milburn, J.A. and Chant, P.D. (1999). Reporting Interests in Joint Ventures and Similar Arrangements. Norwalk, Conn.: Financial Accounting Standards Board for G4 + 1.

Nobes, C.W. (1982). The Gallerani Account Book of 1305-1308. Accounting Review, April, 303-10.

Nobes, C.W. (1983). The evolution of the harmonising provisions of the 1980 and 1981 Companies Acts. Accounting and Business Research, Winter, 43-53.

Nobes, C.W. (1998). Towards a general model of the reasons for international differences in financial reporting. Abacus, 34(2), 162-187.

Nobes, C.W. (2002). An analysis of the international development of the equity method. Abacus, 38(1), 16-45.

Nobes, C.W. and Parker, R.H. (2012). Comparative International Accounting. Harlow: Prentice-Hall.

Parker, R.H. (1989). Importing and exporting accounting: the British experience, in

Hopwood, A.G., International Pressures for Accounting Change, Prentice Hall.

Sangster, A., Stoner, G. and McCarthy, P. (2008). The market for Luca Pacioli’s Summa Arithmetica. Accounting Historians Journal, 35(1), 111-134.

Sombart, W. (1924). Der moderne Kapitalismus, 6th edition. Duncker & Humbolt.

Taylor, R.E. (1980). No Royal Road; Luca Pacioli and his Times, New York: Arno Press.

Yamey, B.S. (1964). Accounting and the rise of capitalism: some further notes on a theme by Sombart. Journal of Accounting Research, 2(1), 33-45.

[FULL] Aricle 6, Volume 3 Issue 1

Process performance management and financial control of key performance indicators as shown in an explorative study of national and international companies

Author

Bettina C.K. Binder

DOI:

10.26595/eamr.2014.3.1.6

Abstract

Process-oriented performance management in international business is a highly debated topic. One question repeatedly raised both in theory and practice is whether process-oriented performance management in national and international firms has to be structured (Tsai and Hung, 2009; Bourne et al., 2003). Moreover, it can be questioned whether the results of key performance indicators measured through financial control in national and international companies are really the same. In order to answer this question, an explorative study (using questionnaires) was conducted by Pforzheim University in the fourth quarter of 2014, in which 26 industrial partners participated. The article, therefore, focuses on the results of the survey which shows what key performance indicators are used (or not used at all) both in national and international companies. The main contribution of this article is the fact that, it shows how the management of key performance indicators in national and international business looks like in reality.

Keywords

1. Process-oriented performance management: introduction

The internal and external accounting of a company should provide, among other things, information in terms of key performance indicators (KPIs) for different audiences. These are measured qualitatively and quantitatively and are hereafter defined as performance measures. Managers and executives in a company as well as internal and external investors make their decisions based on these performance measures (Kaplan and Norton, 2001). If the KPIs are measured on the basis of processes, then a process-oriented performance management system is in use (Bourne, 2000).

It is interesting to compare how many non-financial performance measures or financial performance measures are evaluated in the individual companies (Franco-Santos et al., 2007). Businesses today often calculate the company’s value in order to estimate the intrinsic value of a company. These values typically contain process orientated key data, which makes up a substantial aspect of this article.

In order to determine the substantial key data which is used in financial control in the form of KPIs, an anonymized online survey was conducted in the fourth quarter of 2014 at the Pforzheim University in which 26 industrial partners participated. Many studies have already been done on KPIs. However, no study has been done, unlike the one on this article that compares process orientated performance measures of national and international companies and highlights their commonalities and differences. The study was methodically done by consulting approx. 250 performance measures that are either in use or not in use at all in national and international companies. Subsequently, this was followed by statistical analyses in order to determine the effects of several influential factors on the management of KPIs in a company.

1.1. Performance management and performance measurement: commonalities and differences

Performance management not only focuses on individual business units but also on the management of the entire company by managers and executives (Krause, 2006).

By optimizing operational processes of the entire company, an operational process oriented performance management is achieved (Bititci et al., 2012).

Besides the measurability of KPIs, performance management also considers organizational aspects, for instance, the assignability of resources on different organizational levels, learning effects and employee motivation (Richard et al., 2009). These factors are important in the constellation of the performance measures because they reveal whether the KPIs are simple, standardized or complex and also show the degree of complexity in their calculation (Williams, 2002).

Performance measurement serves as a database for a balanced activity recording through financial control (Gleich, 2011). The objective is obtaining a systematic, multidimensional performance tracking, management and control for different application objects (Bourne, 2001) or on different levels of performance (e.g. employees, teams, departments, processes etc.) with the aim of a continuous improvement of individual company’s performance (Taticchi, Tonelli and Cagnazzo, 2010).

Folan and Browne (2005) focus on the more comprehensive term ‘performance management’ which also includes other management aspects besides measurability. This article will particularly look at performance measurement aspects as a substantial component of performance management used by controllers in a performance measurement system, while the KPIs will be also briefly explained in the following passages.

1.2. Goals and tasks of a process oriented performance management

This article is aimed at achieving a long term performance excellence by measuring the process oriented performance measures and increasing the operational results of a company with an optimal process performance management.

Below are four substantial tasks, which should be fulfilled by process oriented performance measures based on the above study:

Is process orientation guaranteed?
Particularly in global companies, a worldwide standardization of the processes should be aspired so as to, ideally, achieve a process oriented key data that should be uniformly defined and measured globally. This requires, among other things, existing processes and their measurability with the help of process oriented performance measures (Binder, 2003).

Are there approaches to management available?
Performance management mainly targets the holistic management of a company. The goal of an efficient management of an organization is, therefore, to manage the performance provision using performance measures (Demartini, 2014).
Are non-financial aspects integrated in the performance indicators?

Is it only the financial performance measures that are evaluated or also the non-financials? Many publications assume that there is a significant influence of non-financial performance measures on a company’s success (Martín-de-Castro et al., 2011).

Does measurability in national and international companies exist?
It is decisive to have measurability of performance measures not only on the national level of a company, but also on its international operations, if a standardized regulation of key indicators in financial control is to be achieved in order to have uniform control (Neely, 2005).

2. The explorative study based on 26 industrial partners

A research on process performance management and process performance measurement (PPM) models in the literature distinguished between the following concepts:

Financial and non-financial PPM concepts (De Toni and Tonchia, 2001). Here KPIs such as the earnings before taxes (EBT), which is a financial KPI, or the number of claims, or the order process time, which are qualitative measurements, thus non-financial KPIs, can be found.
Strategic and operational PPM concepts (Hoque and James, 2000). These are aimed at creating the right strategy, and derived from it achieving the highest company profit. One of the most famous concepts in this category is the Balanced Scorecard (BSC), designed by Kaplan and Norton in the 1990s. Operational performance measurements work with more detailed KPIs, such as costs of order processing, and they are generally more quantifiable.
Operation oriented and structure oriented PPM concepts (Neely and Adams, 2001). Operation oriented PPM concepts focus on the processes’ stages and their KPIs, while structure oriented PPM concepts go into more detail with regard to company’s organizational structure, analysing for example the function of management control.
Achievement levels and receiver-group-oriented PPM concepts (Purbey, Mukherjee and Bhar, 2007). The PPM concepts included in this category, such as cash flow or KPIS of certain receiver groups (employees, management, etc.) often use relatively individualized KPIs.
Industry related PPM concepts (De Vries and Margaret, 2003). These exist in various industries and branches (metal finishing industry, freight transportation, etc.) and process based KPIs are often integrated and measured in this category.

All five kind of concepts can exist parallel to one another. For instance, a strategic performance management concept for a telecommunications company can be reflected through the financial performance measures. Moreover, the receivers of the key performance information of the individual achievement levels of the company can be represented in teams or departments.

Due to the general misrepresentation of the internationalization degree of performance management concepts, an explorative study was conducted, which should bring light on the performance measures that are used in national and international industrial companies.

The 26 industrial enterprises were carefully selected, they are large companies (with several thousand employees), whose parent company is located in Germany, but have one or more subsidiaries abroad. Moreover, all companies are industrial firms active in different sectors such as food and household products, industrial goods and automotive.

2.1. Application

Not all performance measures in a company are recorded or measured, nor is a standardized collection on a monthly basis done. Thus, the advantage of using a questionnaire is clear since it is easy to show with the help of a questionnaire, whether an industrial company measures the key performance figures on the national level – German parent company- and whether the same figure is used within the international operations or whether the data is recorded at all. Thanks to the greater significance of information and communication technology today, almost any performance indicator can be proven arithmetically (Gladen, 2008). The disadvantage of using a questionnaire, however, is that some types of performance measures such as innovative performance measures (e.g. KPIs for scenario technology) cannot be measured in detail (Kennerley and Neely, 2003). Another example of an innovative performance measure that cannot be measured is the company’s value. This is due to missing value drivers or international procedural input data (Lehtinen and Ahola, 2010).

2.2. Survey

The survey included about 250 performance measures in 9 functional areas. These are: strategic and operational planning, R&D control, logistic control, production control, sales control, cost accounting and calculation, project and investment control and company and shareholding control. As the planning process in a company covers all functional areas and it contains a high number of KPIs which are measurable for most companies, the author chose to concentrate on the functional area strategic and operational planning.

The following illustration shows the structure of the anonymized questionnaire for the area strategic and operational planning:

Figure 1. Anonymized questionnaire on process-oriented performance management.

The return ratio was 42% i.e. 11 participants out of 26 responded. Moreover, there could be multiple answers to one question and therefore the sum of the answers becomes greater than 100%.

2 .3. Financial and non-financial performance measures

The following financial performance measures were, for instance, polled in the functional area strategic and operational planning:

KPIs for strategic gap analysis
KPIs for market and competitor analysis
KPIs for early warning systems
KPIs for life cycle analysis
KPIs for SWOT analysis
KPIs for portfolio analysis
KPIs for balanced scorecard
KPIs for value analysis
KPIs for continuous planning
KPIs for forecasting
KPIs for better budgeting
KPIs for advanced budgeting
KPIs for beyond budgeting
KPIs for annual planning
KPIs for quarterly planning
KPIs for monthly planning
KPIs for benchmarking
KPIs for target costing
KPIs for process analysis
KPIs for value based management
KPIs for core competency approach
KPIs for deviation analysis

(plan/actual/target)

KPIs for deviation analysis

(planning/target agreement)

KPIs for investment projects
KPIs for cost category planning
KPIs for cost objective planning
KPIs for risk planning

The list shows that the performance measures were not broken down into a mathematical formula because this would mean a bigger effort in an already complicated and extensive questionnaire with approx. 250 performance measures. Moreover, the KPIs, especially those in the strategic and operational planning, are closely connected with the instruments that were already of great importance to high-level personnel in financial control (Otley, 2007).

The surveyed non-financial performance measures for the functional area strategic and operational planning were:

Strategic workflows/p.a.
Strategic projects/p.a.
Applied strategic tools/p.a.
Applied strategic IT-solutions/p.a.
Duration of planning period
Capacity planning
Key account customer planning
New customer planning
Number of consolidation workflows

3. Results of the explorative study in national and international companies

The results of the survey in the nine functional areas have confirmed our prior expectations to a large extent: while in the following nine functional areas rather operational performance measures with their associated instruments were evaluated, strategic performance measures and their instruments are, generally speaking, still missing in many companies. Since all the polled companies have their parent company in Germany and own international daughter companies abroad, the survey showed that the performance measures of the parent company were more likely to be measured than that of their international subsidiaries. This means that often no special international performance measures for foreign held companies were used. This fulfilled the expectation of the study that financial control and its key indicators orient themselves particularly to the existing performance measures of the national companies and take over these control figures in the long run for the international holdings alike.

3 .1. Results within the functional area strategic & operational planning

The survey showed that newer instruments like the scenario technology, life cycle analysis and beyond budgeting concepts in the functional area strategic & operational planning (Aureli, 2010) were not used in most companies, thus no performance measures could be evaluated. This also applied to the core competence approach, value based management, target costing and risk management.

As expected, national companies had operational numbers-oriented instruments in use, for instance, planning data for annual and continuous planning, forecast, quarterly and monthly planning and the deviation analysis as well as cost category, cost centers and cost objective accounting. The implementation of such detailed KPIs can be considered typical for German companies. Moreover, it is interesting to note that gap analysis, portfolio and competition analyses, benchmarking and process key indicators were also predominantly used in German companies. Prior to the study, it was assumed that common global strategic instruments and their performance measures were existent in international companies (Jusoh, Ibrahim and Zainuddin, 2008).

It was, however, unexpected to find the use of a small percentage of instruments like value analysis, which incorporates target costing and the value based management, since these are specifically global instruments as can be derived from their name. Moreover, there were no particular instruments and queried performance measures that occur only in international companies.

Above all, financial performance measures are dominant in the functional area of strategic and operational planning. Non-financial performance measures, which are commonly not measured, include the number of workflows, the number of strategic instruments, applied IT-solutions and the number of consolidation workflows.

Least of all, the number of strategic projects, the duration of the planning period, resources in strategic planning are predominantly measured in national companies. The most remarkable here is the measured performance indicator of the number of key account customers and the new customers. The number of consolidation workflow plays no role whatsoever in national and international companies. For the international companies, however, only the non-financial performance measures like the duration of the planning period or the number of key account customers and new customers are relevant for decision-making.

Instruments which are similarly used in national as well as international companies are as per their English definition the early warning systems, i.e. SWOT analysis and the balanced scorecard.

The above evaluation of the functional area strategic and operational planning clearly shows the need of consulting in companies. It also points out which instruments and performance measures that are essential for an effective management in financial control are often still missing (Chow and Van Der Stede, 2006).

The following table summarizes the main results for the functional area strategic and operational planning.

Table 1. Major indicators of the functional area strategic and operational planning

3.2. Management reporting

The functional area management reporting is regarded as a cross section function and multiple key numbers are shown and measured in individual reports. Therefore, the functional areas R&D control, logistics, production and sale control were subordinated to management reporting with the assumption that the reports with the following performance measures are proven and measured.

It is however surprising that in the functional area management reporting of the cash flow per employee and the key number of the shareholder value for all three methods of discounted cash flow, cash flow return on investment and economic value added are not measured (36%). This is contrary to numerous publications as precisely these innovative key numbers are not used in most companies (55%) as the following illustration shows:

Figure 2. Measurement of the key indicator shareholder value in national and International companies.

EBIT and sales key numbers like sales per customer, sales per employee EBIT, EBITA and EBITDA are predominantly measured in national companies (85%). In international companies only 67% use these figures. This is again surprising since these numbers are per definition international key indicators.

Moreover, return key numbers like return on capital employed, return on investment, equity, total capital and turnover profitability are predominantly measured in national companies (82%). In comparison, only 65 % of the international companies use return key numbers. Cash flow is definitely regarded as important in national companies (100%). As expected, performance measures like the liquidity ratio of the first, second and third degree as well as key numbers of the balance sheet like asset cover ratio and consolidated income statement in sales and cost of production method are rather measured in national than in international companies. One possible explanation for this is the availability of data, as in national companies the data is saved in the systems of the companies and the KPIs can be calculated online in time.

The key indicator Working Capital is similarly measured in national and international companies, even though it is per definition an international control key indicator. The measurement of different types of investments (total) or replacement, rationalization and extension investments is also dominant in national companies (100%).

There are no performance indicators that occur only in international companies. In both national and international companies, a shareholder value measurement using the discounted cash flow and economic value added procedure was made, with 36% in each case.

Financial performance measures are dominant in the functional area management reporting and non-financial performance measures such as number of pages of the monthly, quarterly and annual report are not measured; however, the duration up to the dispatch of the monthly, quarterly and annual report in working days is considered. Non-financial personnel key indicators like the employee fluctuation, resources in the form of full-time-equivalents (FTEs) in management reporting and within a capacity planning are predominantly measured in national companies.

The determination of the indicators cash-flow/employee and the shareholder value following e.g. the cash flow return on investment methodology does not take place. Instead, traditional sales results, returns and investment ratios are measured more in national companies. This also applies to the cash flow and working capital, which are rather international figures. This fact can also be connected to the fact that, nowadays, global key figures like the working capital in many companies are commonly measured at the national level, or rather these key indicators have been newly adapted at the national level for measurement. As it can be seen in table 2, there are no key figures that have emerged only in reports of international companies and are absent at the national level.

Table 2. Major indicators of the functional area management reporting

3.2.1 Management reporting R&D control

Although target costing KPIs in the functional area R&D were classified in advance by the companies in the questionnaire to be relevant for international companies with a 36% score, they were in the end not measured. Just a few performance measures are measured within the life cycle costing. Many R&D indicators such as the development rate and the number of R&D projects for basic research and new product development or for product development are hardly used for the measurement. Rarely used are also the number of R&D applications, the approved R&D requests of successful completed R&D projects and the resources expenditure within a R&D project. Likewise, process innovations are not measured as opposed to product innovations.

Typically, most remaining R&D data are measured in national companies (Adams, Bessant and Phelps, 2006). These are the R&D costs in the plan, actual and forecast value (because costs are the easiest KPIs which can be measured), the R&D costs in % of sales and the number of R&D projects. The number of product innovations, as opposed to process innovations, are only measured in national companies (Fitzgerald, 2007).

The indicator time to market was measured both in national and international companies, each with a 55% score.

In R&D control area many qualitative performance measures which are non-financial due to the implemented principles and application-oriented R&D are calculated (Bourne et al., 2000). However, only few qualitative performance measures were actually measured.

The few financial performance measures such as R&D costs in R&D control are measured especially in national companies (91%). Non-financial performance measures are often not measured. What stands out in the functional area R&D control is that, least of all, product innovations and schedule variances are measured in national companies and a capacity planning is made. The average resource requirements for an R&D project in FTEs are mainly measured in national companies.

Table 3. Major indicators of the functional area research and development control

3.2.2 Management reporting: logistics control

Major non-financial KPIs such as the average distance between the storage and the customer are not measured.

Most of the logistics data are measured in national companies. These are financial performance measures such as purchase costs, storage costs and transport costs – each in plan, actual and forecast value because these KPIs show well-defined measures that can be easily calculated. The average stock in kEUR is also measured in national companies. 80% of these performance measures are used for evaluation in national companies. Purchasing costs in plan with a 100% score counts as the most important performance measure in logistics control in national companies.

The purchase costs in plan in international companies are seen as an important performance measure, however, its importance is significantly lower than at the national level. The other performance indicators in logistics control, which occur particularly in reports for international companies, play a rather subordinate role.

One performance measure in the logistics control that is measured with the same intensity at the national and international levels is the backlog. It outlines not in-time delivered commodity (both national and international companies recorded 55% in backlog).

In logistics control section many qualitative performance measures are determined by the procurement, storage and transport activities and their duration (Chan and Qi, 2003). They are usually non-financial. The following qualitative measures of performance are measured: backlog, number of suppliers, scrap rate, duration of customer order processing, number of shipments, average order size, turnover rate and shipping rate. The performance measure turnover rate plays an important role with an 82% score in national companies as well as the rework ratio. The average cash conversion cycle, the number of storage sites and the number of consignment warehouse locations are barely measured.

In the logistics control only a few financial performance measures such as purchase, storage and transport costs are measured particularly in national companies. Table 4 gives an overview of the functional area logistic control.

Table 4. Major indicators of the functional area logistics control

3.2.3 Management reporting production control

The following KPIs were not measured: KPIs for product and process benchmarking (this is surprising since benchmarking and its key data are noted as available in national companies in the questionnaire before – but were left unmeasured), KPIs for decentralized storage and KPIs for technology portfolio analysis. Similarly, KPIs for process cost calculation and KPIs for risk analysis were not measured though it was indicated prior to the survey that data were available, e.g. the later were with a 46% score available both in national and international companies. The degree of asset depreciation was also not measured. It is rather unusual that KPIs for financial statements in production were not consulted for measurement because these KPIs are usually well-defined and often used for measurement.

However, it was seen that financial performance measures were consulted in national companies – in the production area they are the clearest and well-defined KPIs. These are production costs in plan, actual and forecast. Not evident were own performance indicators used in production control, which occur particularly in reports for international companies.

The following performance measures in production control were calculated both in national and international companies: the number of in-time deliveries, the setup time per machine and the production quality – all measured in % (return quantity/production quantity).

Many qualitative performance measures in production control area are calculated but are often not measured. The following non-financial performance measures are used in measurements in national companies: turn-around times for production orders, downtimes per machine, utilization rate per machine, KPIs for central storage and rate of utilization and indicators for capacity planning.

All financial performance measures are consulted for measurements in production control but one: production costs. However, many non-financial performance measures are not used at all. Only three non-financial performance measures play an important role on the national and international level: the number in-time deliveries, the setup time per machine and the production quality. All are measured in % (goods return quantity/production quantity). This analysis suggests that since each company produces a specific product or offers a specific service, it is possible that a specific indicator data will be determined. This was not covered by the existing questionnaire. Thus, financial and non-financial performance measures given in the questionnaire were, for the most part, not used for measurement. An overview of the KPIs in the functional area of production control is presented below:

Table 5. Major indicators of the functional area production control

3.2.4 Management reporting sales control

The present research revealed that KPIs for price calculation in sales control are not available. This is surprising because prices are after all the most important result of the sales control department. However, as expected, financial performance measures were mainly consulted in national companies as they can be measured in time. These are distribution costs (planned, actual, forecast), distribution cost in relation to sales, distribution costs per field staff and average receivables in kEUR.

Furthermore, revenues per market segment and customers, the contribution margin and the profit margin are measured as financial performance measures. The most important performance measures in national companies are revenue, contribution margin and profit margin. These are 100% measured in national companies.

Also, dominant in national companies are the non-financial KPIs like general performance measures for market analysis, product results analysis and customer satisfaction analysis (Vargo and Lusch, 2004). Customer retention analysis key data and KPIs for break-even-analysis, day sales outstanding (although a global use of the key indicators was expected here), number of existing and new customers and key indicators for the capacity planning were measured mainly in national companies.

The following performance measures were calculated in international companies: sales, contribution margin and profit margin, with a score above 81% used for the assessment. Not evident were other performance indicators used in sales control, which occur particularly in reports of international companies.

Sales performance measures that were similarly measured at the national and international level were non-existent.

Just like financial performance measures, non-financial performance measures are applied mainly in national companies. Two performance measures were of great importance in national companies, namely; the number of existing customers and the number of new customers – both accounting to 90% and 100% respectively. Their importance in international companies lies with a score of approximately 72% slightly lower.

Financial performance measures such as distribution costs (plan, actual, forecast), distribution costs in relation to sales, cost of sales per sale representative and average accounts receivable in kEUR, revenues, gross and profit margin were specifically determined in national companies, since these data is saved locally and is therefore easily available (Atkinson et al., 2012). International comparability should be created, for example, the comparison of sales of customer A between Germany and the United States, which often is not the case in reality.

One reason why non-financial performance measures are mainly measured in national companies could be because of the role played by fast operative impact and the direct possibility of influence at the national level (Van Der Stede, Chow and Lin, 2006, Hall 2008). It is possible that a comparability between the KPIs of the national company and international subsidiaries cannot be realized, for example, the number of existing customers cannot be used since this data is collected differently and in different systems. Thus, these key figures are not applied in the international subsidiaries as seen also in table 6.

Table 6. Major indicators of the functional area sales control

3 .3. Cost accounting and calculation

The following KPI is not measured in cost accounting and calculation – shortage costs. It probably involves a logistics specific and process indicator that the surveyed companies did take into account.

However, financial performance measures were consulted in national companies. This is typical for German companies as in the area of cost accounting and calculation nearly all measures employed have a financial background. In the same time, in national companies different non-financial performance measures were also used. Financial performance measures in the functional area cost accounting and calculation include customer success, market segment success, product success, absolute and relative contribution margin, fixed and variable costs, single and overhead costs, plan, actual and target costs, cost category, costs of cost centers and costs of cost units as well as deviation (actual, plan, target).

Other functional costs that are used are write-offs, procurement costs, job planning costs, transport costs, inventory difference, reclamation costs, quality assurance costs and costs for trainings and seminars. The cost increase rate in % is often given in the form of inflation and is therefore increasingly measured at the national level. Due to the fact that operational data such as orders are measured especially in national companies, the costs per order is used as a financial KPI.

Non-financial performance measures also predominantly emerge in the cost accounting and calculation in national companies (Adler, Everett and Waldron, 2000). These are centralized and decentralized booking deadlines (in working days), internal cost allocation (intra, inter and intercompany) (in working days), quotation rates, order rates, reclamation rate in % and capacity.

Some financial performance measures are also used in international companies, in particular: product success, absolute contribution, plan costs, actual costs, deviation (actual, plan, target), as well as write-offs, personnel and procurement costs.
Other control parameters in cost accounting and calculation that occur particularly in reports of international companies are not used.

Only the costs of the customer order processing are present with 55% both in national and international companies. Just like financial performance measures, non-financial performance measures are applied mainly in national companies. The most important figures here are the decentralized and centralized booking deadlines, internal cost allocation as well as order rates and reclamation rates.

Financial performance measures that illustrate costs and have the above mentioned details are typical in German companies. Therefore, it is not surprising that typical German indicators such as profit margin especially in national companies are not only present to 100%, but are also used by their subsidiaries at an 82% rate.

It is possible that the KPIs of the national company and international subsidiaries cannot be compared. For example, the reclamation rate that fall under other reclamation cannot be used standardized since this data is collected using different systems. Therefore, such indicators are hardly used internationally. Table 7 gives a brief account of the situation in the functional area of cost accounting and calculation.

Table 7. Major indicators of the functional area cost accounting and calculation

3 .4. Project and investment control

The following KPIs in project and investment control are not measured: KPIs of static cost comparison calculation and profit comparative calculation, KPIs of process cost calculation, key figures for efficiency analysis, extent of utilization ratio and error rate in the project (in %) as well as the degree of completion of the project in %, the average duration of the investment decision in working days and the variation in quality of the investment projects. These KPIs are considered though in the survey by both national and international companies as important.

However, financial performance measures were consulted in national companies. These are IT-costs, maintenance cost, investment volume, KPIs of static amortization calculation, KPIs of static profitability, KPIs of the capital value method, KPIs of the internal rate of return method, KPIs of the cost-benefit analysis, KPI of project costing and KPIs of the project costs (internal, external).

Non-financial performance measures also predominantly emerge particularly in national companies. Number of investment objects, cost variance of investment objects, time deviation of investment objects (however, a quality deviation of investment objects is not measured). In addition, non-financial performance measures such as capacity planning, project status reports and timeliness of projects are used mainly in national companies.

The financial performance measures IT-costs and external project costs are particularly used in international companies. Further control indicators in project and investment control that arise particularly in reports of international companies were not evident.

The following key indicators are equally found in national and international companies: KPIs of the balanced scorecard and the project completion rate. These performance measures are considered global control instruments (De Geuser, Mooraj and Oyon, 2009).

Just like financial performance measures, non-financial performance measures are applied mainly in national companies. The number and the cost variance of the investment projects, the project status reports, the timeliness of projects (%) and the capacity planning of project resources play a crucial role.

Financial performance measures that illustrate investment costs are typically noted only in national companies. It is surprising that the static procedures (which are usually easy to use) are not used at all (cost comparison calculation or profit comparative calculation), yet dynamic investment computing procedures dominate nationally. As expected, an instrument such as the balanced scorecard which is equipped with financial and non-financial KPIs is used nationally and internationally since its simplicity largely allows international transferability (Davis and Albright, 2004). The project conclusion ratio can be also regarded as simple and is therefore used at the national and international level.

Non-financial performance measures are frequently not taken into consideration since they are deemed as too unusual in this area. Some examples are average duration of the investment decision in working days or quality difference of investment projects. The KPIs used or neglected by the surveyed companies are presented in table 8.

Table 8. Major indicators of the functional area project and investment control

3 .5. Company and shareholding control

The following financial KPIs are not measured in company and shareholding control: KPIs according to US-GAAP (this applies to both the individual as well as the consolidated financial statements), KPIs for the balanced scorecard for shareholding (although in the questionnaire the instrument of the balanced scorecard was described as international), KPIs for the contribution margin accounting for shareholding, value proposition for shareholding, KPIs of process cost accounting for shareholding. In addition, non-financial KPIs such as the page count of the monthly reports on operational, financial and strategic shareholding, the shareholding’s resources and the key figures of the capacity planning are not measured. Furthermore, the KPIs for risks of investments remain side-lined during the measurement.

Most financial performance measures in company and shareholding control were, as expected, mostly measured in national companies because they can be easily measured in time. According to the German Commercial Code (HGB) these are KPIs for individual and consolidated financial statements, KPIs for the balance sheet and consolidated income statements of shareholding and personnel key indicators (capacities) for shareholding. In addition, the following KPIs are measured particularly in national companies: the debt ratio, fixed and current assets as well as equity and liabilities.

Other non-financial performance measures that occur mainly in national companies are: the number of operational, financial and strategic shareholding and the number of shareholding projects.

Performance indicators that are mainly present in international companies are KPIs according to international financial reporting standards (IFRS) for the individual and consolidated financial statements and KPIs for fixed assets, non-current assets, equity and liabilities, each measuring 64%.

Yet, there were no indicators in national and international companies having the same percentage especially in company and shareholding control. Often the area company and shareholding control is one department in an enterprise so that performance measures for the national parent company and the international subsidiary are often measured in the same way.

Non-financial performance measures are often not available or, just like the financial performance measures, they are used mainly in national companies. The non-financial performance measures are mainly indicators used in operational, strategic and financial investments.

The questionnaire did not distinguish between financial control in the parent company and its subsidiaries; this part of the study chiefly displays financial control of subsidiaries. The data gathered in a previous statement of the questionnaire confirmed that innovative key indicators of the balanced scorecard or the process cost calculation, which are not even measured consistently in national companies, are certainly not available internationally, thus, mostly non-existent in the subsidiaries. Predominant are, as it can be seen in table 9, traditional financial and consolidated income statement and proven KPIs like debt ratio, fixed assets, non-current assets as well as equity and liabilities, which can easily be calculated from the balance sheet and consolidated income statement.

Non-financial performance measures are often not measured because they are deemed as too unusual.

Table 9. Major indicator of the functional area company and shareholding control.

4 . Measurement of process orientated performance measures

During the evaluation of the study it became obvious that seven performance measures (out of 10 innovative performance measures that can be seen in figure 3) were mostly measured in national companies. Moreover, key indicators in the international subsidiaries were either omitted or used less frequently. The following illustrations of the functional area strategic and operational planning for national and international companies show these results as a showcase for all other areas:

Figure 3. Performance measures in the functional area strategic & operational planning in national companies.

Figure 4. Performance measures in the functional area strategic & operational planning in international companies.

In addition, it was important to find out which performance measures are not measured in national and international companies in financial control. This possibly calls for the following measures; firstly, a clear definition of these key figures in many companies should be set and, secondly, these performance measure should be made measurable using existing systems. The following illustrations (no. 5 and 6) show 10 most frequently unmeasured financial and non-financial performance measures in national and international companies:

Figure 5. The 10 performance measures in functional area strategic & operational planning in national companies that are hardly used or not used at all.

Figure 6. The 10 performance measures in functional area strategic & operational planning in international companies that are hardly used or not used at all.

Overall, the following performance measures were recorded in the questionnaire:

Key indicators/KPIs total: 242
- Financial process performance measures – total: 156
- Non-financial process performance measures – total: 86
- Process orientated control indicators – specific: 127
- Non-process oriented control indicators – specific: 115

127 process orientated performance measures were established which were characterized by a non-financial database and a comprehensive process orientation in the cross section function of a company, since one main focus of the research laid in the field of process orientation. It was surprising that process orientated indicators were frequently not measured and most companies wished they could do so by proving process management with the help of process orientated indicators. Furthermore, in all functional areas, except for the area sales control, the financial key indicators outweighed non-financial ones.

This shows, particularly with process-oriented indicators, that companies need further consultancy so as to define these performance measures and to outline their use. This general impression that KPIs are predominantly measured in national companies could also be confirmed by the survey for process orientated indicators. But even in national companies, process orientated performance measures were incomplete and were only shown upon request. In sales control process oriented indicators were mostly used. Of the 17 performance measures more than half (11) were process oriented and 6 purely financially as the following illustration shows:

Figure 7. Number of financial and process orientated key indicators.

5. Conclusion

The overall result of the study can be summarized in five points:

Five key performance indicators: EBIT (operating profit), cash flow, shareholder value (EVA), annual planning (operating profit), IFRS (financial statements, operating earnings) were employed for financial control in all companies both at the national and international level. Essentially, it became obvious that there is need for further research and consulting pertaining the practical use of process orientated performance measures.

KPIs for core competency approach, innovative KPIs scenario techniques, the life cycle analysis, the better, advanced and beyond budgeting are non-existent in most companies. This is surprising because in literature these concepts are mentioned as innovative and often used. Most companies state that they have no key indicators for value analysis, target costing for value based management and risk management (although these instruments have the highest percentage for use internationally). Examples of key indicators that are hardly measured are: cash flow/employee and shareholder value as well as R&D applications and other non-financial functional key indicators. Furthermore, utilization rates and KPIs of process cost calculation are often not used. It is clear that the use of innovative instruments is aspired by many companies, but the practical application in most companies is still missing.

As expected, the following indicators are present in national companies: planning data (continuous planning, forecast, for quarterly and monthly planning and for deviation analysis) as well as KPIs of information on cost type, cost center and cost unit. The reason is that especially in German companies detailed performance measures are used. Also measured in national companies are process and benchmarking indicators. It is not surprising that, above all, EBIT, sales and return numbers are used in national companies because they are exactly measurable in time. As expected, particularly functional costs such as R&D costs are measured in the corresponding functional control areas like R&D control, logistics control, production control and sales control.

In national and international companies early warning systems, the SWOT analysis and the balanced scorecard are in use because these instruments are often applied internationally. They also make up a large part of the non-financial measures particularly in financial control.

It is striking that international companies have no special reports for special indicators (exception: KPI time to market in working days). This suggests that international companies use national control indicators. Thus, from the financial control point of view, performance measurements and performance management should not be considered separately since their control measures are similar.

The results of the survey were based on German parent companies and their international subsidiaries, who participated in the study. The findings of this study suggest that there is need for further research. For instance, the next step could be based on this survey; parent companies could be surveyed in order to identify their needs for different performance measures to be used in financial control within the management of KPIs.

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[FULL] Aricle 5, Volume 3 Issue 1

Well-being and engagement as predicted by fit and misfit of work to family supervisor interruption behaviors and – subordinate interruption tolerance

Author

Mireia Las Heras Maestro

Sowon Kim

Pablo Ignacio Escribano

Anneloes Raes

DOI:

10.26595/eamr.2014.3.1.5

Abstract

The purpose of our study is to examine the effects of alignment and misalignment between supervisor’s work-to-family interruptive behaviors and subordinate’s tolerance for such interruptions on the latter’s well-being and engagement. Based on 582 supervisor-subordinate dyads, we find that alignment (the degree of similarity between the supervisor and subordinate) is not always significantly better than misalignment. In fact, our results show that when supervisor shows a low level of work-to-family interruptive behaviors while subordinate has a high tolerance for such interruptions increases subordinate’s well-being and engagement. When supervisor shows a high level of work-to-family interruptive behaviors while subordinate has a low tolerance for such interruptions, subordinate’s well-being and engagement decrease. Implications and future research directions are discussed.

Keywords

1. Introduction

A growing body of the literature shows that work-family interruptions and tensions have grown over the past decades (Chinchilla, Las Heras, & Masuda, 2009; Poelmans, Greenhaus, & Las Heras, 2013). Simultaneously, the options to work in a more flexible manner (from alternative locations and at different times) have also grown exponentially. This all has resulted in employees who are potentially required to respond to communications and solve queries 24/7, who are better equipped to work from places other than the office, and who thus need to solve the dilemma of when, and to what extent, they let their job intrude in their family time (Kossek & Lautsch, 2008; Sweet, 2014). The work-to-family interface is about time spent at work, work tempo, scheduling, autonomy and predictability among others. It specifically is about managing to what extent work-to-family boundaries are permeable; specifically to what extent work intrudes into family time.

While some individuals are comfortable with such interruptions, others are not, as individuals differ in the degree to which they prefer to have their work and family life segmented or integrated (Nippert-Eng, 1996). Individuals who prefer to segment work and family life have a tendency to separate the two spheres and are more likely to be disturbed by interruptions than those who prefer to integrate. However, preferences might not always translate into actual behaviors. That is, how the work-family boundaries are managed might not only depend on what I prefer or the extent to which I can tolerate interruptions, but also on what is expected from me and what I am allowed to do or not do. In a work relationship, i.e., supervisors and subordinates, both persons might perceive and manage work-to-family interruptions in a similar (aligned) or dissimilar (misaligned) way.

Alignment refers to the degree of compatibility and similarity between the individual and his/her environment, represented by the job itself, the values of company or the supervisor’s personality (Kristof, 1996). Alignment is widely accepted as being desirable due to its positive outcomes such as job performance, job satisfaction, organizational identification, citizenship behaviors and turnover decision (e.g., Bretz & Judge, 1994; Cable & DeRue, 2002; Jansen & Kristof-Brown, 2006; Wang, Zhan, McCune, & Truxillo, 2011). Yet, no study to our knowledge has examined the person-supervisor fit in the work-family context and specifically, how this (mis)fit might impact an individual’s work and family life.

The purpose of our study is to examine the effects of (mis)alignment between supervisors’ actual behaviors of work-to-family interruption (hereby supervisor interruption behaviors) and subordinates’ tolerance for such interruptions (hereby subordinate interruption tolerance) on subordinates’ (a) well-being and (b) engagement. To do this, we build on the literature on boundary theory, as well as the literature on person-environment fit. In this section, we pay special attention to distinguish the phenomenological approaches to fit and situate our study in the correct one. Second, we build on the literature on leader-follower (mis)fit, and based on boundary and fit theories, we draw hypotheses regarding the effects of supervisor-subordinate interruption management (mis)alignment. We then proceed to present our method and results and to discuss them. Finally, we offer practical implications, state the limitations and propose directions for future research.

The present study contributes to the literature by looking at a so far unexplored phenomenon, the fit between supervisor interruption behaviors and subordinate interruption tolerance, which promises to be a source of relevant insight for both research and practice on work-family interruptions. We do so using 582 supervisor-subordinate dyads belonging to two companies in El Salvador. Our results provide significant results, specifically on the consequences of misalignment, which may considerably enhance insights in the area of work-family research and have implications for managerial practices.

2. Literature review, theory and hypotheses

2.1. Managing Work-to-Family Interruptions

As postulated by boundary theory (Nippert-Eng, 1996), individuals draw boundaries between work and family life and manage them proactively (Ashforth, Kreiner, & Fugate, 2000; Clark, 2000; Nippert-Eng, 1996). Role boundaries are physical, temporal, emotional, cognitive and/or relational limits that define those roles as separate from one another, thus delimiting its perimeter and scope (Ashforth, Kreiner, & Fugate, 2000). The extent to which individuals are comfortable with, and allow interruptions from, one role intruding the other, depends on whether they prefer to segment or to integrate work and family life (e.g., Ashforth et al., 2000; Kreiner, Hollensbe, & Sheep, 2009; Nippert-Eng, 1996).

Segmentors see the two roles as separated; they prefer keeping activities of each role within their respective boundaries (e.g., not solve personal issues at work); and they are capable of cognitively disconnecting (e.g., not think of work at home). Integrators see the two roles as interconnected; thus, they draw permeable and flexible boundaries between them (e.g., work at home, make personal calls at work). Research has failed to show that segmentors are more attracted toward companies offering segmenting policies, while integrators feel more attracted by companies offering integration policies (Bourhis & Mekkaoui, 2010). However, it presents evidence that the desire for greater segmentation moderates the relationship between the organizational policies one has access to and an individual’s satisfaction and commitment; specifically, people who want more segmentation are less satisfied and committed to the organization when they have greater access to integrating policies than when they have less access to such policies (Rothbard, Phillips, & Dumas, 2005).

The extent to which individuals segment versus integrate is defined by the characteristics of the boundary, which can be described by their level of permeability and flexibility. A boundary is permeable when individuals are physically and cognitively present in one role and interrupt it to carry out an action that belongs to the other role — such as taking a phone call from a family member while being at work or sending an email to a doctor to make an appointment (Clark, 2000). A boundary is flexible when individuals want to leave one role to attend to the interruptions of another role, and are capable of doing so (Matthews & Barnes-Farrell, 2010). Researchers have also shown that people can create their ideal level of segmentation or integration by using various tactics (Kreiner et al., 2009), suggesting that they are actively involved in boundary management.

Both segmenting and integrating boundaries have shown to have positive as well as negative consequences. Segmenting decreases work-family conflict, but also limits positive spillover. Integrating enhances positive, but also negative, spillover (e.g., Hecht & Allen, 2009). The primary benefit of segmenting is the reduced ambiguity and interruptions between domains; these benefits are actually the primary costs of integrating. The primary cost of segmenting is the increased difficulty to cross boundaries between the two domains; a reduced difficulty to cross boundaries is actually the primary benefit of integrating (Ashforth et al., 2000).

Individuals differ in the ways they manage their boundaries between work and family (Kreiner, Hollensbe, & Sheep, 2009), as a result of individuals’ variance in their preferences to segment or to integrate (Rothbard et al., 2005), and workplaces’ variance in the extent to which an environment favors segmentation or integration (Hochschild, 1997; Kossek & Lautsch, 2012; Kreiner, 2006). For example, offering onsite day care or promoting an integrating work climate during the recruitment process encourages integration at the workplace (Hochschild, 1997; Kossek, Noe, & DeMarr, 1999). On the other hand, policies such as flexible schedules promote segmentation (Rothbard et al., 2005). Segmenting and integrating styles are most effective when individuals’ boundary management preference and workplace policies are congruent (Rothbard et al., 2005). The alignment between what individuals prefer and perceive to get from their workplace is hence more important than the boundary management style per se (Kreiner, 2006), underling the importance of context.

Context influences, and restricts, how the work-family interface is managed (e.g., Allen, 2001; Anderson, Coffey, & Byerly, 2002; Lapierre & Allen, 2006; Voydanoff, 2005a). Supervisor behaviors are a particularly relevant aspect of context for employees. More specifically, family supportive supervisor behaviors (Hammer, Kossek, Yragui, Bodner, & Hansen, 2009) facilitate the use of work-family policies by employees (Blair-Loy & Wharton, 2004; Casper, Fox, Siztmann, & Landy, 2004) and decrease work-family conflict (Frye & Breaugh, 2004). Supervisors might also influence subordinates’ ability to set their preferred levels of work-to-family interruption. Employees might work for managers who, by how they behave, signal to what extent they expect a certain level of work-to-family permeability. We argue that the level of the supervisor’s work-to-family interruption signals and creates expectations for the subordinate’s need to interrupt family time for work reasons. However, the supervisor’s style might collide with the subordinate’s permeability preferences and thus level of tolerance. When that misalignment happens negative consequences might follow.

2.2. Person-Supervisor Fit and Atomistic-Objective Fit

Individuals who fit the work environment are more satisfied with their job (Bretz & Judge, 1994; Jansen & Kristof-Brown, 2006), are more attached to their organization (Cable & Judge, 1996; Westerman & Cyr, 2004) and tend to perform better (Lauver & Kristof-Brown, 2001; Wang, Zhan, McCune, & Truxillo, 2011). A great deal of research has looked at the effects of fit between the person and his/her environment, whether this be the organization, the job or the workgroup (for a comprehensive review of older articles look at (Kristof, 1996). Person-Supervisor Fit (P-S fit) refers to the compatibility between subordinates and their supervisors. Researchers have explored dyadic leader-follower types of fit such as: personality fit (Van Vianen et al., 2008; Zhang, Wang, & Shi, 2012), values fit (Brown & Trevifio, 2009; Meglino, Ravlin, & Adkins, 1991) as well as the effects of the fit on specific values dimensions such as power distance (Cole, Carter, & Zhen, 2013).

Paying attention to who assesses the source of information assessing the fit, and to whether fit is measured directly or indirectly, it can be classified as (Kristof, Zimmerman, & Johnson, 2005): perceived fit, which refers to the direct assessment of fit by the individual; subjective fit, which refers to an indirect assessment of fit — that is P and E variables are rated separately — by the individual; and objective fit, which refers to an indirect assessment of fit by different parties. Studies of P-E fit are generally referred to as investigations of the same phenomenon regardless of whether they assess perceived, subjective or objective P-E fit (Kristof, 1996).

Edwards, Cabe, Williamson, Lambert, & Shipp (2006) differ in their conceptualization of the types of fit and classify them as: atomistic, molecular and molar. In their words “Atomistic studies assess the perceived person and environment separately; molecular studies assess subjective P-E discrepancies that combine the person and environment but preserve the direction of their difference, and molar studies assess perceptions of P-E fit that combine the person and environment and disregard the direction of their difference, treating positive and negative discrepancies as equivalent in terms of P-E misfit” (p. 803).

These two conceptualizations of the types of fit differ in their approach and overlap to a certain extent. Molar is equivalent to perceived fit, yet molar can be potentially rated by a third party; atomistic fit can be both objective and subjective; finally, Kristof and colleagues’ (2005) typology does not refer to the molecular type of fit, which refers to direct assessment of misfit, i.e., discrepancy, and could potentially be rated by the individual or by a third party. This actually highlights that fit studies should specify to which type of fit refer so that they contribute to the nomological network. However, as Edwards and colleagues (2006) claim, most fit research treat all these phenomenon as if they were interchangeable, leaving many inconsistencies and holes in the nomological network. The present study will focus on the atomistic-objective fit, which has been under-studied as it is most difficult to measure.

There is some evidence that types of fit have different consequences, and potentially different antecedents. For instance, Turban and & Jones (1988) investigate the consequences of molar-subjective supervisor-subordinate fit, atomistic-objective fit regarding important behaviors to receive merit pay raise and demographic similarity of individual characteristics. They found that although the three types of fit were related to subordinate performance, they differently predict job satisfaction and pay ratings. Hayibor, Agle, Sears, Sonnenfeld, & Ward (2011) found that molar-perceived fit, i.e., followers’ perceived value congruence between themselves (members of top management teams) and their leaders (CEOs), is positively related to followers’ perceptions of the degree of charisma possessed by the leader. However, they found that results of atomistic-objective fit, i.e., the self-reported value of the supervisor and the self-reported value of the subordinate are measured separately and then a measure of value congruence is developed, has almost no influence to perceptions of charismatic leadership.

2.3. (Mis)Alignment between Supervisor Interruption Behaviors and Subordinate Interruption Tolerance

Research shows that supervisors shape subordinates’ experiences. For instance, Cole, Bruch, & Vogel (2006) show that amidst an organizational crisis, a subordinate’s positive and negative emotions fully mediated the relationships between perceived supervisor support and cynicism, and a subordinate’s psychological hardiness and cynicism. Eisenberger, Singlhamber, Vandenberghe, Sucharski, & Rhoades (2002) demonstrate that when supervisors show support, subordinates perceive the entire organization as being more supportive, and this relationship is stronger the higher the supervisor is in the organizational hierarchy. In a time in which interdependence between supervisors and subordinates is becoming crucial for organizational performance, understanding the antecedents and consequences of (mis)alignment of supervisor-subordinate characteristics is more urgent. On the one hand, for subordinates, alignment might be important because they might be aware that their supervisors are subject to a similarity bias, and thus they want to comply with their expectations to be viewed in a more positive light. For instance, the experiment conducted by Deprez-Sims & Morris (2010) found that decision makers have a bias against those perceived lower in similarity.

On the other hand, subordinates are subjected to role-sending signals from the supervisors, who communicate to their employees certain verbal and nonverbal cues regarding their expectations. Supervisors, acting as role senders, communicate expectations regarding role behavior to their subordinates pressuring them to comply with expectations (Greenhaus & Powell, 2003). According to Greenhaus & Powell (2003) supervisors might create stress when they expect subordinates to participate in a specific work activity while they participate in a family activity. The supervisor’s (role sender) behaviors might therefore be strong cues that implicitly communicate role expectations. Similar to what happens with cultural values, supervisors’ behaviors influence subordinates’ values through “norming” and “conforming” regulatory effects of organizational values (Caprar & Neville, 2012). The “norming” effects of supervisors’ behaviors might shape what subordinates perceive as acceptable in terms, for instance, of hours worked and acceptable work-to-family interruptions. “Conforming” effects of managerial behaviors refers to the legitimizing consequences of those behaviors, for instance by making phone calls to home landlines, a form of work-to-family interruption, look legitimate.

To date, little is known regarding the effects of (mis)alignment between role-sender cues and the focal person’s tolerance for interruption, leaving it unknown to what extent it matters, and to what extent researchers and practitioners alike should be concerned about, and therefore, act upon it. To fill this gap in the literature, our research focuses on the atomistic-objective fit of supervisors and subordinates. We consider supervisors and subordinates as separate entities and we collect data from two sources — self-reported levels of supervisor interruption behaviors from work to home and self-reported subordinate interruption tolerance coming from work to home — and study the effects of such alignment / misalignment on subordinates’ well-being and engagement. We specifically focus on work-to-family interruptions because work-to-family and family-to-work interruptions do not need to — and usually are not — equally matched (Kossek & Lautsch, 2012) and, managerial work-to-family interruption behaviors are likely to signal to employees to what extent they are expected to act in a similar way. Specifically, when subordinates receive communications at non-work standard times, they’ll notice that supervisors are working and probably interrupting their own family time. This will become a signal of what is expected and welcomed.

Supervisors could, to some extent, ameliorate this signaling effect by communicating, for instance, that they are not required to respond until they get to the office, or that the answer can wait to the next workday. However, even in cases where that signaling exists, research shows, and subordinates tend to know that supervisors prefer and tend to promote and evaluate in a more positive light, similar others, i.e., that supervisor and subordinate congruence in how they perceive the demands and characteristics of the work environment is linked to greater subordinate satisfaction and higher performance ratings (Turban & Jones, 1988). Thus, supervisor interruption behaviors will still be relevant for the subordinate even if the supervisor clarifies the expectations about required responses to his/her interruptions.

Even though people might experience interruptions differently, interruption are a critical factor in job stress (Kirmeyer, 1988). Research shows that they are usually experienced negatively (Rogelberg, Leach, Warr, & Burnfield, 2006), that they mediate the effect of objective work volume on the feeling of overload (Kirmeyer, 1988), and that work-related communications outside of regular work is associated with higher levels of work-to-family conflict, distress and sleep problems (Schieman & Young, 2013; Voydanoff, 2005). Although most research on interruptions refers to those happening in the work domain, mutatis mutandis, negative effects might occur when it is work that interrupts family time, i.e., that greater effort devoted to the care for the interruptions depletes the resources that could have been allocated to family; that progress toward completion of the primary task in the family role slows down; and that as a result the person feels increased fatigue and negative mood. In this study, we examine two individual outcomes, well-being and engagement.

2.4. Well-being and Engagement in Work-Family Research

Extensive empirical evidence demonstrates that experiences stemming from work and family roles affect individuals’ well-being which involves simultaneous experience of high positive affect and low negative affect, and is related to good health (Wright, Cropanzano, & Bonettet, 2007). In the work-family literature, well-being does not only refer to affective and physical dimensions but also embraces job satisfaction, marital satisfaction, family satisfaction and life satisfaction (Allen, Herst, Bruck, & Sutton, 2000; Greenhaus, Bedeian, & Mossholder, 1987). It has been shown consistently that role interference decreases well-being (e.g. Allen, et al., 2000; Thompson, Brough, & Schmidt, 2006), whereas support systems stemming from families and the work environment (such as work supportive families and family friendly supervisor behaviors), protects it (Lapierre & Allen, 2006; Mauno, 2010).

Engagement is a “persistent, positive affective motivational state of fulfillment in employees that is characterized by high vigor, dedication and absorption” (Maslach, Schaufeli, & Leiter, 2001: 417). Vigor indicates high levels of energy and mental resilience while working and the willingness and ability to invest effort in one’s work. Dedication denotes being strongly involved in one’s work and experiencing a sense of significance, enthusiasm, inspiration, pride and challenge. Absorption means being fully concentrated and happily engrossed in one’s work, whereby time passes quickly and one feels carried away by one’s job. Engagement refers to a positive fulfilling work-related state of mind, which differs from organizational commitment and job involvement (Hallberg & Schaufeli, 2006). While engaged individuals involved in extra work roles such as organizational citizenship behaviors experience higher work interference with family (Halbesleben, Harvey, & Bolino, 2009), work engagement also facilitates work-family relations via the sharing of positive work experiences at home (Culbertson, Mills, & Fullagar, 2012). Research shows that perceived workplace flexibility is related to greater employee engagement (Pitt-Catsouphes & Matz-Costa, 2008; Richman, Civian, Shannon, Hill, & Brennan, 2008).

We expect that the alignment between supervisor interruption behaviors and subordinate interruption tolerance will lead to well-being and engagement. First, by the way supervisors behave subordinates will gather that their preferred boundary management style is acceptable. Second, subordinates will see themselves as similar to their supervisor. In addition, we expect that the alignment between supervisor interruption behaviors and subordinate interruption tolerance will be always experienced as more positive than the misalignment between interruption behaviors and tolerance. We also expect that more positive effects will follow when the alignment is at the extremes; i.e., when supervisors and subordinates are clearly inclined toward interruption behaviors and interruption tolerance, or toward no-interruption behaviors and interruption intolerance. Thus, we hypothesize that:

Hypothesis 1. The more aligned a subordinate’s tolerance for interruption is with his or her supervisor’s level of interruption behavior (i.e., higher alignment) the higher the level of (a) well-being and (b) engagement of the subordinate.

Hypothesis 2. When alignment between the subordinate’s tolerance for interruption and the supervisor’s interruption behaviors is at high or low levels, subordinate’s (a) well-being and (b) are higher than when alignment is at an intermediate level.

Although there are some scarce evidence that misalignment, for very specific outcomes and in very unique situations, might be beneficial (Roth, Kostova, & Dakhli, 2011), it is generally accepted that misalignment is experienced negatively. Supervisors are role senders who, by interrupting their family time with work, signal to subordinates to what extent work should be allowed to intrude into family time. We can imagine a subordinate who most of the time he does not overlap with his supervisor’s schedule. If he sees then that the supervisor contacts him, or others, while she is supposed to be at home, he’ll know that the supervisor is interrupting her family time. The opposite case, in which the subordinate and the supervisor are most of the time working on the same schedule, the supervisor might actually interrupt the subordinate’s, or any peer’s, family time making it evident she is working after hours. One can easily imagine the strength of the message when supervisors send communications at atypical hours, during major national festivities or when they are supposedly on holidays. All these will be experienced more negatively when subordinate interruption tolerance is lower than supervisor interruption behaviors. On the other hand, when supervisors do not interrupt as much as the subordinate is willing to tolerate the interruption, the misalignment might be experienced positively. Thus, we propose that:

Hypothesis 3. Misalignment between the subordinate’s tolerance for interruption and the supervisor’s interruption behaviors are associated with a low level of subordinate’s (a) well-being and (b) engagement.

Hypothesis 4. When supervisor’s interruption behavior is higher than the subordinate’s interruption tolerance, subordinate’s (a) well-being and (b) engagement are lower than when supervisor’s interruption behavior is lower than the subordinate’s interruption tolerance.

3. Method

3.1. Participants and Procedure

Data were collected as a part of a larger project on work-family conciliation, yet none of the variables used in this paper have been used for other projects. Members of the International Work and Family Center (ICWC) from a Spanish university contacted two organizations located in El Salvador and invited them to participate in the study. In exchange, the companies were offered a complete analysis reporting the degree to which their work-family practices have been implemented and future steps that can be taken to improve (or sustain) their actual condition. In order to increase the response rate, we asked companies to sponsor our research and to let respondents complete the survey during their work-time. The survey was web-based and located on a secure server at the Spanish university.

In both organizations a supervisor headed each work unit. We distributed the surveys — along with a cover letter assuring confidentiality and voluntary participation — to 101 supervisors and 952 subordinates. Among them, 85 supervisors (84.16%) and 787 subordinates (82.67%) returned the survey. We were able to match 582 supervisor-subordinate dyads, which represents an average span of control of 6.85 subordinates per supervisor. Among the final sample of supervisors, 30 were female (35.29%), with an average age of 39.34 years (SD 8.12), and with an organizational tenure of 13.17 years (SD 7.92). Among the subordinates, 312 (53.61%) were female, with an average age of 29.55 years (SD 8.39), and with an organizational tenure of 6.08 years (SD 5.85).

3.2. Measures

All the scales used in our survey were initially developed and validated in English but the survey was administered in Spanish. To ensure conceptual equivalence, we followed the approach suggested by Harkness and colleagues (2003). A bilingual researcher translated the questions into Spanish. This version was then back-translated into English by another bilingual researcher, and the back translation was compared with the English original. Revisions were made by a local country collaborator to ensure conceptual equivalence (Harzing, Reiche, & Pudelko, 2013). All the scales were measured using a 7-point Likert scale in which 1=strongly disagree and 7=strongly agree.

Supervisor work-to-family interruption behaviors. We measured supervisor interruption behaviors using the four item scale developed by Kossek et al. (2012). This scale captures the extent to which individuals (supervisors) allow interruptions from one role to another (work-related issues outside work hours). A sample item of this scale is “I spend time and energies in work issues while I’m engaged in family or personal activities.” In our study, this scale reported a reliability of .91.

Subordinate work-to-family interruption tolerance. We measured subordinate interruption tolerance using a modified version of the four item scale developed by Kossek et al. (2012). In order to capture the preference for keeping both roles separate, i.e., the tolerance toward those interruptions, we added “I prefer not to” to every item of this scale before referring to interruption behaviors. A sample item of the modified scale is “I prefer not to spend time and energies in work issues while I’m engaged in family or personal activities.” As we sought to test the alignment with supervisor interruption behaviors, we inverted the subordinates’ responses in order to capture the work-to-family interruption tolerance (and not the intolerance). We inverted the responses by subtracting the subordinates’ responses to eight because we measured this construct using a 7-point Likert scale. The reliability of this measure in our study is .85.

Subordinate well-being. Subordinates responded to the three item well-being scale developed by Vansteenkiste and colleagues (2007). A sample item is “In general, I am satisfied with my life.” The reliability of this measure in our study is .86.

Subordinate engagement. Subordinates responded to the nine item engagement scale developed by Schaufeli and Bakker (2003). This scale measures three dimensions of engagement: vigor, dedication and absorption. In the present study, we found that the three dimensions were highly correlated (an average r of .69). Therefore, we conducted an exploratory factor analysis (EFA) of the nine items representing the three dimensions and found only one factor with an eigenvalue greater than 1.0. Therefore, we created a single engagement index using the nine items of the scale. This is in line with previous research that also found that a single dimension fits the data better than the hypothesized three-dimension scale (e.g., Shimazu et al., 2008; Sonnentag, 2003). A sample item is “At my work, I feel bursting with energy.” The reliability of this measure in our study is .91.

Control variables. Prior research suggests subordinate-level outcomes may be related to similarity in leader and follower demographic characteristics such as age and gender (e.g., Li & Bagger, 2011; Perry, Kulik, & Zhou, 1999). Therefore, following previous research, we controlled for the (dis)similarity in these variables in our analyses. Dissimilarity in age was operationalized as difference score between the leader and the follower (Perry, Kulik, & Zhou, 1999). In the case of gender similarity, we used a dummy variable (0 = “different gender” and 1 = “same gender”) (Li & Bagger, 2011).

3.3. Analysis

Polynomial regressions. To test our hypotheses, involving alignment and asymmetrical misalignment effects, we used polynomial regressions and response surface modeling (Edwards & Parry, 1993). We followed this procedure in the current analysis because it reduces methodological issues involved in difference scores (Edwards, 1994). Specifically, the dependent variables (well-being and engagement) were separately regressed on control variables as well as five polynomial terms. That is, subordinate interruption tolerance (F), supervisor interruption behaviors (L), subordinate interruption tolerance squared (F-squared), subordinate interruption tolerance times supervisor interruption behaviors (F x L), supervisor interruption behaviors squared (L-squared). To reduce multicollinearity and facilitate interpretation of the results, we scale-centered all the independent variables (Edwards & Parry, 1993), which involved subtracting a value of four to our independent variables since all of them were measured on 7-point Likert scales.

After we performed the polynomial regressions, we plotted predicted values using the response surface technique for interpreting the results. These surfaces are shown in Figures 1 and 2, where well-being and engagement were graphically represented — respectively — as a function of subordinate’s work-to-family interruption tolerance and supervisor’s work-to-family interruption behaviors. For building these graphical representations, we used the coefficients of the polynomial regressions as well as the post-estimation covariance matrices as inputs (Shanock, Baran, Gentry, Pattison, & Heggestad, 2010). Additionally, we used these inputs to examine the slopes and curvatures along two important lines — the alignment line and the misalignment line. The shape of the surface along alignment line is obtained by substituting the formula for this line (F = L) into the polynomial regression equation, including all the points where the subordinate’s tolerance for interruption is matched with their supervisor’s interruption behaviors. In Figures 1 and 2 the alignment line is from the front corner (where F=L=-3) to the rear corner (where F=L=3). Similarly, the shape of the surface along the misalignment line is obtained by substituting the formula for this line (F =
-L) into the polynomial regression equation, including all the points where the subordinate’s tolerance and supervisor’s behaviors are in perfect disagreement. In Figures 1 and 2 the misalignment line is from the left corner (where F=-3; L=3) to the right corner (where F=3; L=-3). For estimating these lines, their shape (curvature) and their respective significance we used the spreadsheet developed by Shanock and colleagues (2010) .

4. Results

Table 1 shows the means, standard deviations, correlations, and reliability coefficients of the variables.

Table 1. Means, standard deviations, correlations, and cronbach’s alphas (n=582)

4.1. Construct Validity

We conducted confirmatory factor analyses (CFAs) to examine the distinctiveness of the three subordinate self-reported variables (i.e., tolerance for work-to-home interruption, well-being, and engagement) doing maximum likelihood estimation in STATA 12 (StataCorp, 2011). We started by examining a 3-factor model where subordinate tolerance for integration, well-being and engagement, were each loaded onto separate factors. We compared this 3-factor model with 4 alternative models, including: (a) a 2-factor model where well-being and engagement loaded onto a single latent factor; (b) a 2-factor model where tolerance for interruptions and well-being loaded onto a single latent construct; (c) a 2-factor model where tolerance for integration and engagement loaded onto a single latent construct; and (d) a 1-factor model where all the items loaded onto a single latent factor. Results are summarized in Table 2. Overall, results of the confirmatory factor analysis revealed that the 3-factor model yielded a satisfactory fit [chi-square (99) = 560.226; p < .001; RMSEA = .089, CFI = .930, TLI = .997, SRMR = .035] and a significantly better fit than the alternative models. Therefore, the 3 subordinate-reported factors considered in our model were indeed distinct latent constructs.

Table 2. Confirmatory factor analysis of nested models (n=582)

Hypotheses Testing

Table 3. Polynomial regression results predicting subordinate well-being and engagement (n=582)

Hypothesis 1 predicted an alignment effect of subordinate tolerance for interruption and supervisor level of interruption behaviors such that the higher the alignment the higher the (a) well-being and (b) engagement. Table 3 presents the estimated coefficients as well as the slopes and curvatures along the congruence and incongruence lines for the polynomial regression predicting well-being. As shown in Table 3, the curvature of the congruence line is positive and significant (.054; p<.1) whereas the curvature of the incongruence line is positive and significant (.079; p<.01), suggesting that both lines are non-linear and convex (U-shaped). As mentioned above, the highest level of alignment is captured by the alignment line. The convex curvature along the misalignment line indicates that subordinate’s well-being may be higher in some cases when deviating from the alignment line; thus, we do not find support for the alignment effect for the case of well-being. Similarly, Table 3 indicates that when predicting engagement the curvature of the alignment line is positive and significant (.074; p<.05), whereas the curvature of the misalignment line is positive and significant (.052; p<.1). The convex curvature along the misalignment line indicates that subordinate’s engagement may be higher in some cases when deviating from the alignment line; thus, we do not find support for the alignment effect for the case of engagement. In sum, we do not find support for the alignment effect suggested in Hypothesis 1.

Figure 1. Well-being as predicted by work to family supervisor interruption behavior – subordinate interruption tolerance

Hypotheses 2 predicted that (a) well-being and (b) engagement are higher when both the subordinate and supervisor are aligned at high or at low levels of interruption tolerance and behaviors, rather than when they are aligned at mid-levels of interruption tolerance and behaviors. As shown in Table 3, when predicting well-being, the slope along the alignment line is non-significant (.014; n.s.). Thus, we cannot conclude that well-being increases as the alignment takes place at higher levels of tolerance towards integration. Moreover, because the curvature of the alignment line is positive and significant (.054; p<.1), well-being increases more sharply when the alignment takes place at more extreme points — when both the subordinate and supervisor are aligned at high or at low levels of interruption tolerance and behaviors. As shown in Figure 1, well-being is higher at high/high (rear corner) and low/low (front corner) alignments than at any point in between the alignment line. Similarly, as shown in Table 3, when predicting engagement, the slope along the alignment line is non-significant (.029; n.s.) and the curvature is positive and significant (.074; p<.05). As shown in Figure 2, engagement increases more sharply when both the follower and leader are aligned at high (rear corner) or at low (front corner) levels of interruption tolerance and behaviors than in intermediate cases. Therefore, we find support for Hypothesis 2.

Figure 2. Engagement as predicted by work to family supervisor interruption behavior – subordinate interruption tolerance

Hypothesis 3a predicted that the more misaligned a subordinate’s level of interruption tolerance is with supervisor’s level of interruption behaviors (i.e., higher misalignment), the lower the level of subordinate’s well-being. As mentioned above, when predicting well-being the curvature of the misalignment line is positive and significant (.079; p<.01). A positive slope of the misalignment line (U-shaped) suggests that as the misalignment between the predictor variables (i.e., subordinate’s interruption tolerance and supervisor’s interruption behavior) increases, the outcome variable (i.e., well-being) increases rather than decreases (Shanock et al., 2010). Similarly for Hypothesis 3b, when predicting engagement the curvature of the misalignment line is also positive and significant (.052; p<.1). In sum, although we find an effect of the size of the misalignment on our outcome variables, these results counter our expectations. Thus, we do not find support for Hypothesis 3.

In Hypothesis 4 we predicted an asymmetrical misalignment effect. More specifically, Hypothesis 4a predicted that the expected negative misalignment effect on well-being is lower when supervisor’s interruption behaviors are higher than the subordinate’s tolerance rather than when the subordinate’s interruption tolerance is higher than the supervisor’s behaviors. When predicting well-being the slope of the misalignment line is significant and positive (.193; p<.001), indicating that well-being is higher in the region where the subordinate’s tolerance is higher than the supervisor’s interruption behaviors (right side of the plot). Indeed, as seen in Figure 1 well-being is the highest in the right corner of the surface plot, where subordinate interruption tolerance is the highest while the supervisor interruption behavior is the lowest. This is also the case for Hypothesis 4b. When predicting engagement the slope of the misalignment line is positive and significant (.191; p<.001), which means that engagement is higher in the region where the employee’s tolerance is higher than the supervisor’s interruptive behaviors. This asymmetrical effect is also shown in Figure 2, in which engagement is lower at the left corner (where F=-3; L= 3) than at the right corner (where F= 3; L=-3). Therefore, we find support for Hypothesis 4.

5. Discussion

The overall objective of this paper is to shed light on the alignment of subordinate’s work-to-family interruption tolerance and supervisor’s work-to-family interruption behaviors, and assess the implications of (mis)alignment for subordinate’s well-being and engagement. Our empirical study tests 582 supervisor-subordinate dyads and provides some interesting and surprising insights that may considerably advance knowledge in the work-family research area, and have implications for managerial practices.

5.1. Integrating Expected and Unexpected Empirical Findings

Based on boundary management and person-environment fit theories (Ashforth et al., 2000; Kristof, 1996; Nippert-Eng, 1996) we had postulated that alignment between subordinate interruption tolerance and supervisor interruption behaviors would have more positive relationships with employee well-being and engagement than misalignment (i.e., Hypothesis 1a and 1b). However, our findings indicate that such alignment is not always significantly better than the misalignment. On the contrary: testing Hypothesis 3a and 3b, we showed that incongruence has a significant positive effect on well-being and engagement, rather than the hypothesized negative effect. These unexpected findings may indicate that the beneficial effects of congruence, or fit, between subordinates and supervisors on other dimensions such as values or personality (Brown & Trevifio, 2009; Van Vianen et al., 2008), are more nuanced when considering the alignment between subordinate interruption tolerance and supervisor interruption behaviors. These results may also speak to some recent calls from researchers that have asked for examining congruence and incongruence effects separately, not assuming that congruence and incongruence are opposite ends of the same continuum, and not assuming that perceptions of the person and environment as separate entities (atomistic fit) are the same as directly assessing the difference (molecular) or the similarity (molar) between the person and the environment (Edwards et al., 2006).

Before examining the more nuanced explanations for this unexpected finding, we consider our further results. Based on our empirical tests analyzing the significance of the non-linear effects of alignment and the asymmetrical effects of misalignment in the response surface patterns (Edwards, 1994; Harris, Anseel, & Lievens, 2008), we could confirm the remaining Hypotheses 2 and 4. Alignment between subordinate’s tolerance and supervisor’s behaviors that occurs at either high or low levels of interruptions, as compared to a medium level, is positively related to subordinate well-being and engagement (i.e., Hypothesis 2a and 2b). Misalignment where supervisor’s interruption behaviors are higher than the subordinate’s interruption tolerance is more detrimental for employee outcomes than a misalignment in which the subordinate’s interruption tolerance is higher than the supervisor’s actual interruption behaviors (i.e., Hypothesis 4a and 4b).

Taken together, these findings suggest four scenarios that result in the best and worst consequences for employees’ well-being and engagement. The first and best case scenario is when subordinate well-being and engagement are highest due to misalignment in which a subordinate has a high tolerance for interruptions and the supervisor reports low interruptive behaviors. In the scenarios that are second-best and third-best in terms of employee outcomes, subordinate tolerance and supervisor behaviors are aligned, i.e., about the same level. Finally, the fourth and worst scenario is when subordinate well-being and engagement are lowest due to misalignment in which subordinate has a low tolerance for interruptions and the supervisor displays high interruptive behaviors. For the scenarios two to four, the theorizing as put forward in the theory section of this paper provides a valid explanation. That is, supervisor-subordinate congruence signals to employees that their preferred style for boundary management, i.e., keeping work from intruding into family, is acceptable, and indicates a similarity between the subordinate’s tolerance and the supervisor’s behaviors. Such perceived acceptance and similarity in turn relates to a higher level of well-being and engagement.

5.2. Integrated Theoretical Framework: Conservation of Resources Theory

Our postulated theories, however, cannot integrate an explanation for our first scenario, suggesting that additional theory development is necessary. In particular, we propose that a self-regulation mechanism may underlie the effects for congruence. The Conservation of Resources (CoR) theory outlines how people’s responses to threatening or stressful situations can be explained by the resources they have available for coping with the situation (Hobfoll, 1989; Quinn, Spreitzer, Lam, 2012). Since the situation of balancing work-family demands, and especially handling interruptions, has often been identified as a potential stressor (Greenhaus & Beutell, 1985), CoR theory seems applicable to understand our current question. Moreover, this theory does not contradict our theoretical reasoning as outlined before, but rather provides an underlying rationale for integrating this initial theorizing with our unexpected empirical findings (cf. Halbesleben, Wheeler, & Rossi, 2012).

Based on CoR theory, we suggest that handling work-family interruptions is a situation for which people need resources to cope with — just as many other demanding situations at work ask for putting in resources — and that depletes energy (Hobfoll, 1989; Quinn, et al., 2012). Since people have an inherent tendency to conserve their energetic resources, they will strive to handle the situation in a way that requires the lowest possible level of resources (Quinn, et al., 2012). Prior research has indicated that employees can move resources between work and family domains, and that their boundary management strategies are consequential for the generation and depletion of resources (Halbesleben et al., 2012).

For the interaction between subordinate interruption tolerance and supervisor interruption behaviors, this implies that it is most beneficial when a subordinate has a high tolerance for interruptions (because with such high tolerance, dealing with interruptions will require a relatively low level of energy), and when a supervisor shows a low level of interruptive behavior (because no extra energy is required to deal with those interruptions). For example, when an employee does not mind that her supervisor interrupts family time, and yet this person observes that the supervisor hardly ever does so, the employee does not waste energy worrying about whether the supervisor will interrupt, and simultaneously since interruptions do not occur, she does not spend energies handling them. The worst possible scenario would be observed when a subordinate has a low tolerance for interruptions, and simultaneously the supervisor displays a high level of interruptive behaviors. In this case the expectation of interruptions stresses the employee, plus the actual handling of interruptions consumes his energies. These predictions are exactly what we observe in scenario 1 and 4.

Although the scenarios 2 and 3 could be understood from our original theorizing, they are also consistent with our integrated theorizing of CoR, boundary theory and fit theory. Because of the postulated benefits (i.e., acceptance and similarity) of congruence between employee preferences and supervisor behavior, we can theorize that a situation of congruence has energetic benefits. Employees enjoy such benefits, because they need to spend fewer resources on worrying about how their supervisor will perceive and judge them, as well as on potential impression management behaviors toward the supervisor. Such perceived energy benefits can in turn be expected to translate into higher well-being and engagement.

6. Implications for practice

Work and family have historically been regarded as “separate worlds” (Kanter, 1977; Pleck, 1976). However, our results show that they are more inter-connected than one would expect, i.e., even the way managers’ operate in their “non-work time” affects employees’ well-being and engagement. This indicates that supervisors’ use of their spare time has a symbolic affect on employees, and that they should know they are role senders even when they work away from work space and time. Managers’ work from non-work spaces and times might thwart — or foster — employees’ ability to manage their own work-family interface, and in turn their well-being and engagement.

Our results suggest that companies might benefit from having clear HR policies that help in reducing employees’ uncertainties about what is “acceptable” behavior in terms of contacting others and making it visible that people are working. This should specially target managers, so that they understand when, and to what extent, it is acceptable to make visible that they work at non-work standard hours. Managers should know the consequences that their behaviors might have on others, imposing burdens that show to actually be detrimental for the company. There are companies that are doing things that might help in this regard: like programming emails so that even if they are written at non-work hours they reach the target person at work-hours.

Finally, our results suggest that even when employees clearly tolerate interruptions, they are much better off if they are not interrupted. Thus, training employees to raise their tolerance might be a good option that will give the best results if later the employee is not interrupted.

7. Limitations and future research

The limitations of our study come from different sources. First, potential limitations to the external validity of our results come from the sample of subordinates belonging to two organizations from the service sector located in only one country (El Salvador). Due to particular national characteristics that might affect such results, any study done in only one country calls for caution when generalizing the results. More specifically, El Salvador is characterized by being culturally high in power distance (Hofstede, 2001). Researchers have proposed that in such cultures congruence effects might be higher because subordinates tend to monitor more the supervisor’s characteristics and working styles and align to them (Zhang et al., 2012). In our study this would imply that congruence would have had a stronger effect on outcomes compared to people from countries lower in power distance orientation. However, our findings show that actually misalignment may play a more important role than alignment, so that a bias toward congruence is not a concern in our data. Taking into account the influence of culture on how people manage the work-life interface (Powell, Francesco, & Ling, 2009), future research should look into the role of specific dimensions of culture, or other national context variables, on the impact of alignment of subordinate’s tolerance for work-to-family interruptions and supervisor’s actual work-to-family interruption behaviors. This would facilitate a more nuanced understanding of our findings.

Second, the cross-sectional nature of our data might, to some extent, prevent us from making conclusions about causality. However, this is not such a concern in fit studies, in which both components of the fit may vary as a function of time in non-equivalent ways, rendering cross sectional designs less susceptible to spurious results than in other types of research (Lee & Antonakis, in press).

Third, although related to the previous one, the work units surveyed in our study were established before we gathered the data. According to the ASA — attraction-selection-attrition — model (Schneider, 1987; Schneider, Goldstein, & Smith, 1995), workgroups and organizations become more homogeneous over time, thus it might be that those employees who perceive that their supervisors interrupt more than they are willing to tolerate abandoned the organizations before we gathered the data. However, if this is the case, homogeneity would convey that our data might be biased toward subordinates with higher tolerance for interruptions, so the fact that we still find significant results suggests that our findings are rather conservative.

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Advanced production practices and performance: empirical evidence from Spanish plants

Abstract

Keywords

1. Introduction

2. Antecedents and hypotheses

2.1. Total Quality Management (TQM)

2.1.1. Customer Involvement (CI)

2.1.2. Supplier Quality Improvement (SQI)

2.1.3. Process Emphasis (PE)

2.2. Just In Time (JIT)/Lean Manufacturing (LM)

2.2.1. Just in Time Delivery by Suppliers (JTDS)

2.2.2. Kanban System

2.2.3. Equipment Layout (EL)

2.3. Total Productive Maintenance (TPM)

2.3.1. Autonomous Maintenance (AM)

2.3.2. Preventive Maintenance (PM)

2.3.3. Maintenance Support (MS)

2.4. The relationship between the non-financial operational indicators and the financial indicators

3. Methodology

4. Results

4.1. Phase 1: Evaluation of measurement model (outer model)

4.2. Phase 2: Evaluation of the structural model (inner model)

5. Discussion

6. Final considerations and future research

This research has been partly funded by the Spanish Ministry of Science and Innovation, project DPI-2009-11148, and by the Junta de Andalucía project P08-SEJ-03841. The authors wish to acknowledge both Governments’ support.

References

Corporate governance and performance during plummeting and soaring financial markets

Abstract

Keywords

1. Introduction

2. Prior literature and hypotheses development

2.1. Multiple blockholders

2.2. The identity of controlling owners

2.3. Firms controlled by families

2.4. Firms controlled by financial institutions

3. Sample and data

4. Model and variable specification

4.1. Dependent variable

4.2. Explanatory variables

4.3. Control variables

5. Results

6. Conclusions and Limitations

References

The international transfer of technology: Examples from the development of accounting

Abstract

Keywords

1. Introduction

2. The invention and spread of double entry

2.1. Before double entry

2.2. The invention of DEB

3. Published, audited financial statements

4. Consolidation

5. The equity method

6. The true and fair view

7. The second and income statement

8. Conclusions

References

Process performance management and financial control of key performance indicators as shown in an explorative study of national and international companies

Abstract

Keywords

1. Process-oriented performance management: introduction

1.1. Performance management and performance measurement: commonalities and differences

1.2. Goals and tasks of a process oriented performance management

2. The explorative study based on 26 industrial partners

2.1. Application

2.2. Survey

2.3. Financial and non-financial performance measures

3. Results of the explorative study in national and international companies

3.1. Results within the functional area strategic & operational planning

3.2. Management reporting

3.3. Cost accounting and calculation

3.4. Project and investment control

3.5. Company and shareholding control

4. Measurement of process orientated performance measures

5. Conclusion

References

Well-being and engagement as predicted by fit and misfit of work to family supervisor interruption behaviors and – subordinate interruption tolerance

Abstract

Keywords

1. Introduction

2 .3. Financial and non-financial performance measures

3 .1. Results within the functional area strategic & operational planning

3 .3. Cost accounting and calculation

3 .4. Project and investment control

3 .5. Company and shareholding control

4 . Measurement of process orientated performance measures