An analysis of CSR in human resource management practices and its impact on employee job satisfaction in Catalonia, Spain

Author

Dolors Celma

Esther Martínez-García

Josep Mª Raya

Abstract

Incorporating Corporate Social Responsibility (CSR) into HRM may be a way of integrating employee wellbeing within the workplace. This article provides an empirical analysis of the impact CSR human resource management practices (HRMP) have on job satisfaction (JS). Our aim is to determine which practices contribute most to enhancing employee JS. In an attempt to be as comprehensive as possible, the analysis includes a broad array of HRMP and different dimensions of JS as well as overall JS. Results taken from a representative sample of 1,647 Spanish employees in Catalonia show that in general CSR in HRMP increases JS; some practices (such as wage level) are only relevant for specific dimensions of JS, while others (such as job security) stand out due to their positive effects on all or the majority of JS dimensions. Implications for policymakers and firms interested in attracting and retaining employees by creating high JS are discussed.

Keywords

  • Job satisfaction
  • Corporate Social Responsibility
  • Human resource management
  • Dimensions of job satisfaction
  • Job quality

1.      Introduction

Fostering job quality is a key element of the European social model (European Commission: COM, 2001-313; COM, 2003-728 and COM 2005-24) and job quality itself is on the Europe 2020 agenda. It is considered that employees with high levels of job satisfaction (JS) and higher quality jobs contribute to firms’ competitiveness (European Commission, 2003; 2008). From an academic perspective, there is growing concern and debate around quality of work, ethics in human resource management (HRM) and employee wellbeing (see for example, Cloutier-Villeneuve, 2012; Rhodes and Harvey, 2012; Spencer, 2013). Incorporating Corporate Social Responsibility (CSR) into HRM may be a way of integrating employee wellbeing within the workplace. CSR embraces many theories and approaches, leading to difficulties in making the concept operational (Dahlsrud, 2008; Dobers and Springett, 2010; Ellis and Bastin, 2011; Garriga and Melé, 2004; Lee, 2008; Lindgreen, 2010). However, national and international bodies and institutions have contributed to making CSR less diffuse. In practice, parameters for socially responsible labor management are basically defined through the conventions, principles, regulations, directives, etc. drawn up by the diverse international bodies and institutions that promote CSR and on which national bodies and institutions base their own recommendations, such as: the ILO’s labor conventions; the European Commission’s “Green paper” from 2001 (European Commission, 2001a) and its COM 2001, COM 2002 and COM 2011 (European Commission, 2001b; 2002; 2011); the Global Compact program, the Global Reporting Initiative (GRI), ISO 26000, the OECD’s Guidelines, and the Social Accountability International certification (SA 8000) accrediting social responsibility.

 

With regard to CSR with employees, we will take the above recommendations and norms as a basis for our analysis of the effects of CSR in human resource management practices (HRMP) on JS. Although these documents do not offer a single list of “socially responsible” HRMP, they do include the main areas of action related to job quality (Celma, Martínez and Coenders, 2014): remuneration, recruitment, training, internal information and communication, health and safety in the workplace, equal opportunities and non-discrimination, and work/life balance. To be socially responsible in terms of job quality refers to taking into account both the objective characteristics of individual jobs and those relating to the general work environment (Cloutier-Villeneuve, 2012; European Commission, 2001b). According to the GRI and EU recommendations, it is socially responsible to reduce temporary contracts and increase job security (award permanent contracts), provide employee training that allows the employee to develop, and award an equitable remuneration, which in some cases will include variable remuneration or profit sharing based on effort or results. It is also responsible to improve the firm’s internal information, employee knowledge and participation in the firm’s policies, eliminate discrimination of any type in the workplace, promote health and safety at work, and introduce practices for the adequate conciliation of work and personal life.

 

In the Spanish case, and specifically that of Catalonia, one of the country’s more economically active regions and also active in terms of implementing CSR actions, very little is known about the practice of CSR in HRM (Celma et al., 2014). Despite an initial willingness to implement CSR in Spain, public administrations reacted incohesively due to a lack of joint common criteria for organizing CSR (Murillo, 2008). Nevertheless, Catalonia is among the most dynamic subscribers to CSR. In its “2009–2012 Plan RSCat-Generalitat de Catalunya 2009” and “2014-2020 Strategy Catalonia 2020” program, the regional government emphasized human capital development as one of the values required by twenty-first century organizations. They included strategies such as promoting job security, qualification levels, safety at work, equality, and training. Numerous examples of good practices already exist in many firms in Catalonia. However, they are not enough to delineate generally applicable conclusions regarding the adoption and impact of CSR practices, especially in the area of HRM. There is also a shortage of both academic and informative research providing clear and broad empirical evidence (Celma et al., 2014; Diller, 1999).

 

The aim of this article is to empirically analyze the impact of CSR in HRMP on employee JS. In doing so it will contribute to knowledge regarding the following: which practices have the highest and most significant impact on JS; which practices do not seem to have any impact; and which affect only specific dimensions of JS and which have a broad effect, impacting on many or all dimensions of JS. JS is therefore analyzed using different dimensions (JS with intrinsic factors, contextual factors, time factors, non-money compensation, wages, and job security) and as a single measure, or overall JS. The article is structured as follows: after this introduction, section two reviews the results obtained from previous empirical research on JS, which contribute to generating the hypotheses of this research. Section three is devoted to data and methods, section four to results and section five includes the conclusion and discussion.

 

2.      Literature review and research hypothesis

The mainstream perspective of human resource management studies has offered a large amount of empirical research on the determinants of employee JS. JS has been considered a means for increasing employee involvement and commitment and hence contributing to firms’ competitiveness and performance. Accordingly, the practices analyzed have basically been those considered to be high-performance work practices: related to increasing employees’ means of and opportunities for developing their skills. These are seen as positively affecting JS and the firm’s performance (Rhodes and Harvey, 2012). Increased JS is mentioned in Appelbaum et al. (2000), via practices for worker autonomy, working in teams, communication and opportunities for greater employee involvement; in Harley et al. (2007), via improved selection processes and appropriate training; and in Macky and Boxall (2007, 2008), via internal promotions and being involved in decision making processes. Other studies go beyond these practices: Edgar and Geare (2005) found that practices which improved physical safety, equal opportunities for employees and training were crucial in raising JS. Sousa-Poza and Sousa-Poza (2000) also found positive effects with increased job safety, while Goldman et al. (2006) found the same with non-discrimination and equal opportunities. Guest (2002) found evidence that work-family balance programs, along with improved information within the firm, training and measures to prevent harassment, were positive for workers’ JS; Gius (2012) analyzed the effects of unionization on the satisfaction of teachers in the US; Macky and Boxall (2007, 2008) included working more hours, heavier workloads and greater demands from the firm in their research, which were found to reduce JS. As for wages, Hamermesh (2001) found wages had a positive effect on JS, while Clark (1996) did not find statistically significant effects and Clark and Oswald (1996) obtained negative effects (cited in García and García, 2006).  One of the reasons that can explain literature findings variety concerning the impact of salaries on job satisfaction is the fact that employees may value their salaries in relation to a reference, such as the expected salary, instead of in absolute terms. Therefore, a growing number of studies on job satisfaction are including salaries as a subjective rather than an objective variable (Clark, 2005; Sousa-Poza and Sousa-Poza, 2000).

 

In the case of Spain, empirical studies have generally found a positive relationship between HRMP that enhance job quality and employee JS. Borra and Gómez (2012) show that JS is increased with practices that enable personal and work-life balance, job training and higher salaries, and negatively affected by health-risky jobs. Gamero (2010) found, in his analysis of differences in JS among immigrants and Spanish natives, that satisfaction was mostly affected by job rather than personal characteristics. Rico (2012) found the same, and also that gender was significant as an explanatory variable, women being more satisfied than men. Similarly to Gamero (2010), Sánchez-Sallero et al. (2014) found that demographics were not as relevant if job quality characteristics were included in the analysis of JS. In their analysis of JS among university graduates and the effects of over-education and over-skilling, among other results Kucel and Vilalta-Bufí (2013) found that JS increased with learning opportunities and with having time for family tasks. Finally, Pouliakas and Theodossiou’s (2010) research on JS in several countries found that low-paid employees were significantly less satisfied with their jobs than high-paid employees in Spain. All of the above studies used data for the whole of Spain, with none focusing specifically on the case of Catalonia, one of the most economically active regions. Moreover, the above did not consider dimensions of JS, only overall JS. These two aspects are considered in the present analysis: data refer only to Catalonia, and different dimensions of JS are analyzed. However, our previous literature review is very useful since it shows that there seems to be a general positive relationship between better HRMP and JS. Therefore, we hypothesize that this will also be the case in Catalonia: 

 

H1: CSR labor practices have a positive influence on job satisfaction. Since there is not enough information and consensus as to which practices are more influential for JS, one of the main objectives of the present analysis is to empirically determine these in the case of Catalan employees.

With regard to JS, most studies have taken it as a unidimensional concept (overall JS). However, this is still compatible with the fact that its cause can be multidimensional and it is possible to distinguish between different dimensions (Gamero, 2003; Harzberg et al., 1959; Rose, 2003; Spector, 1997). For example, Rose (2003) distinguished between extrinsic and intrinsic factors. The former depends on the working environment (such as wage level and promotion opportunities), while intrinsic factors are more conditioned by personal characteristics and qualitative aspects of the job. There are also studies which measure job quality with synthetic indicators that also include dimensions (Boccuzzo and Gianecchini, 2015; Díaz-Chao, Ficapal-Cusí and Torrent-Sellens, 2015). Therefore, in this analysis we also consider dimensions of JS besides overall satisfaction. Firms interested in enhancing employee satisfaction need to know which HRMP have a greater effect on each satisfaction dimension, and if there are practices with broader effects, that is, affecting more than one dimension.

Given that JS can have different dimensions and that HRMP are clearly related to some of these, the following research hypothesis will be analyzed:

 

H2: Different CSR HRMP will have different effects on specific dimensions of JS. Some HRMP will be more relevant than others in the sense that they will affect more than one dimension.

If this is the case, firms, governments and employee representatives can direct their efforts towards those HRMP that increase the satisfaction dimension they are most interested in and those which are more effective in terms of affecting a wider set of dimensions.

The third hypothesis refers to the relevance of HRMP and personal characteristics to JS. The literature review showed that when HRMP are included in the analysis, employees’ personal characteristics lose relevance as determinants of JS (Gamero, 2010; Rico 2012; Sánchez-Sallero et al., 2014). However, specific dimensions of JS may be affected by personal characteristics, as it is in the case of intrinsic characteristics (Rose, 2003). Therefore, the following hypothesis is proposed:

 

H3: JS will depend mainly on HRMP, but some personal characteristics of the employee can still be relevant.

In the analysis, we will also include explanatory variables other than personal characteristics, which will act as controls, such as characteristics of the firm’s and the economic sector in which it operates.

 

3.      Data and methods

Information on labor practices and the characteristics of firms, jobs and employees is taken from the Quality of Work Life Survey (QWLS), conducted in 2007 by the Spanish Ministry of Labor on a representative sample of employees aged 16 or above in Catalonia, Spain. The present analysis is based on n=1,647 respondents. The QWLS asks employees about their jobs, working conditions, etc. in different areas, as well as their satisfaction with specific aspects. When employees are asked for an assessment the scale generally ranges from 0 (none/worst) to 10 (most/best). National surveys on working conditions have a number of characteristics that make them especially useful for analysis, and they have therefore been used in previous studies, such as those conducted by Celma et al. (2014), Gamero (2010), Kalmi and Kauhanen (2008), Harley (2002), Peccei (2004), Ramsay et al. (2000) and Rico (2012). First, they provide a broad sample from the wide spectrum of business, work and personal realities and work practices that apply in the studied territory, making it possible to study beyond the scope of specific industries, firm types, or employee profiles. Moreover, being representative of the employee population, they allow researchers to generalize results to a greater extent than when the samples smaller in size, leading to a high statistical power and representativeness. Additionally, as an essential feature of our analysis, the QWLS survey provides information as it is perceived by employees, which allows us to analyze the degree and type of “responsible” practices and the results of these practices from the employees’ perspective. Similarly to previous related studies (Guest, 1998, Kehoe and Wright, 2013; Rousseau and Greller, 1994; Wright and Nishii, 2007), we are interested in employees’ perceptions because they are the directly affected agents and their perceptions affect their attitude and behaviours, and hence their JS.

HRM were selected from the QWLS to reflect information on CSR practices according to directives and recommendations from the international bodies promoting CSR mentioned in the previous section. They include practices in the areas of recruitment, training, remuneration, internal information and communication, health and safety in the workplace, equal opportunities and non-discrimination, and work/life balance. The specific practices are as follows: in the area of responsible recruitment, type of contract (fixed/temporary) and “full-time versus part-time work” – in Spain, fixed contracts provide great job stability and most part-time jobs are less advantageous for the employee than full-time ones. To reflect responsible training, “the company offers training” (yes/no) was selected. For equitable remuneration, “participation in profits” (yes/no) and type of remuneration (fixed, variable or mixed) were selected. CSR considers variable remuneration and profit sharing according to effort or results to be an equitable practice if well and transparently designed. Social remuneration (number of social benefits received by the employee) was also included in the analysis. Monthly wage received by the employee was also included, although unfortunately there was no measure of equitable remuneration. To reflect responsible information provided to the employee and knowledge of and participation in the firm’s policies, we used a variable for internal information in the firm regarding its goals, organization and the training it offers, and another variable regarding working in teams (this is considered to potentially increase knowledge and participation). As for no discrimination of any type in the workplace, we used two variables: “level of discrimination and harassment”, which includes discrimination by sex, age, nationality and disability and psychological and sexual harassment; the other variable used was “maternity harms the professional career”. For health and safety at work, our variables were environmental workplace conditions and safety at work. And finally, for an adequate balance of work and personal life, we used the variables “working longer hours”, “difficulty getting leave” and the number of weekly hours spent at work. Some of the HRMP variables have values ranging from 0 (none) to 10 (maximum), while others are categorical (e.g. yes/no; or quantitative (e.g. number of weekly hours spent at work). Table 1 provides their descriptive statistics and the type of variable (categorical, quantitative, etc.) for each. Mean and standard deviation are shown for quantitative variables and the percentage for qualitative variables. Cronbach-alpha was used to analyze the internal consistency of the grouping of practices and generation of new synthetic variables, such as, for example, in the case of level of discrimination and harassment (also shown in Table 1). JS was regressed on the HRMP and control variables. The latter are shown in Table 2 and are related to firm and employee characteristics (such as size of firm and industry in which it operates, and employee age, gender and level of education).

 

Descriptive sample statistics show that most Catalan employees had a fixed, permanent and full-time work contract, did not participate in benefits, did not work in teams, and received relatively low salaries. The number of social benefits was low on average, but so were the perceived level of discrimination and harassment and risk level at work, as well as difficulty obtaining leave. The variables with higher values are perceived safety at work and environmental workplace conditions. 

VARIABLES*

Average

(Standard Deviation)

Percentage

N

Type of remuneration

 

 

1,647

Fixed

 

81.1

1,335

Variable o mixed

 

18.9

312

Participation in profits

 

 

1,647

Yes

 

16.8

276

No

 

83.2

1,371

Number of social benefits (between 0 and 10)

1.32 (1.933)

 

1,647

Monthly wage

 

 

 

< 600€

5.9

97

1,647

600-1,200€

49.4

813

 

1,201-2,100€

35.6

587

 

2,101-3,000€

7.2

119

 

>3,000€

1.9

31

 

Type of contract

 

 

1,647

Fixed

80.1

1,319

 

Temporary

19.9

328

 

Time work

 

 

1647

Full time

 

86.6

1427

Part time

 

13.4

220

Company offers training

 

 

1,647

Yes

 

52.3

861

No

 

47.7

786

Internal information

(summated Scale) (Cronbach’ α = 0,85) (0=none; 10= much)

6.17 (2.901)

 

1,647

       Information about organization chart

6.45 (3.208)

 

1,647

       Information about company goals

6.40 (3.244)

 

1,647

       Information about training offer by company

5.66 (3.403)

 

1,647

Team work

 

 

1,647

Yes

 

80.3

1,322

No

 

19.7

325

Risk level (0=none; 10=much)

3.61 (3.250)

 

1,647

Environmental workplace conditions (summated scale)

(Cronbach’ α = 0,77) (0=very bad; 10=very well)

7.03 (2.170)

 

1,625

Air conditioning evaluation

6.28 (3.575)

 

1,336

Heating evaluation

7.13 (3.001)

 

1,385

Ventilation evaluation

7.36 (2.730)

 

1,448

Noise evaluation

6.11 (3.290)

 

1,622

Lighting evaluation

8.07 (2.062)

 

1,500

Physical space evaluation

7.87 (2.271)

 

1,498

Safety provided by company

(summated scale) (Cronbach’ α = 0,87) 0=never; 10=always)

7.48 (2.791)

 

1,647

Information about labour risks

7.36 (3.073)

 

1,647

Safe working conditions

7.59 (2.842)

 

1,647

Level of discrimination and harassment

(summated scale) (Cronbach’ α = 0,79) (0=none; 10=much)

 

 

1,647

Level of discrimination by sex

0.55 (1.711)

 

1,647

Level of discrimination by age

0.45 (1.495)

 

1,647

Level of discrimination by nationality

0.46 (1.503)

 

1,647

Level of discrimination by disability

0.26 (1.142)

 

1,647

Level of psychological harassment

0.51 (1.660)

 

1,647

Level of sexual harassment

0.15 (0.793)

 

1,647

Maternity harms the professional career (1= Yes; 2 =No)

 

 

1,647

Maternity damages professional career of women % of Yes)

 

25.3

759

Paternity affect professional career of men % of Yes)

 

26.2

888

Working longer hours

0=never; 10=always)

3.62 (3.283)

 

1,647

Weekly hours at work

39.39 (8.440)

 

1,647

Difficulty getting leave

(summated scale) (Cronbach’ α = 0,87); 0=none;10= much)

3.61 (3.114)

 

1,611

Difficulty to request unpaid leaves

3.42 (3.512)

 

1,594

Difficulty to request long leaves

3.85 (3.637)

 

1,587

Difficulty for reducing working hours

3.94 (3.671)

 

1,583

Difficulty to request short absences

2.94 (3.446)

 

1,511

*The mean and standard deviation are calculated for quantitative variables and the percentage for qualitative variables.

Table 1. Descriptive Statistics of CSR in HRM

 

VARIABLES

Percentage

N

Industry type

 

1,647

Primary Sector

0.9

15

Manufacturing

22.7

374

Construction

12.1

200

Services

64.3

1,058

Company size

 

1,647

Micro

23.2

382

Small

24.5

404

Medium

18.1

298

Large

34.2

563

Sector

 

1,647

Public sector

21.0

346

Private sector

79.0

1,301

Age

 

1,647

< 25 years

6.7

111

25- 44 years

56.4

929

45- 55 years

23.9

393

> 55 years

13.0

214

Gender

 

1,647

1.      Man

53.9

888

2.      Woman

46.1

759

Level of education

 

1,647

3.      No studies/ Primary education

22.0

362

4.      Secondary education

31.4

517

5.      Professional education

21.7

358

University education

24.9

410

Birth place

 

1,647

Catalonia

69.3

1,141

Rest of Spain

16.4

270

6.      Immigrant

14.3

236

Professional category

 

1,647

Staff

79.2

1,305

Manager (first level)

9.7

159

Manager (second level)

8.4

138

Top manager

2.7

45

Seniority in the firm

 

1,647

< 1 year

16.6

274

1-2 years

18.8

309

3-5 years

15.7

259

6-10 years

17.2

284

11-20 years

17.4

286

> 20 years

14.3

235

Table 2. Descriptive statistics of company and workers variables

 

With regard to the dependent variable JS, the QWLS included an overall JS question measured between 0 (none) and 10 (maximum). Table 3 shows its descriptive statistics. Most employees were satisfied (around 70% marked 7 or more on the questionnaire), which is in line with the results found in Eurofound (2007).

 

 

Job satisfaction

N

%

%

Cumulated

0 (none)

7

0.4

0.4

1

5

0.3

0.7

2

11

0.7

1.4

3

34

2.1

3.5

4

44

2.7

6.1

5

167

10.1

16.3

6

218

13.2

29.5

7

358

21.7

51.2

8

475

28.8

80.1

9

192

11.7

91.7

10

136

8.3

100.0

Total

1,647

100.0

 

Average (standard deviation)

7.19 (1.748)

 

 

Table 3. Descriptive statistics of job satisfaction

 

The QWLS also included questions regarding satisfaction with different job aspects, such as wage level and job security. Table 4 includes the 19 considered here, which have been subjected to a principal components analysis to reduce the number of variables and find dimensions of JS.

Rotated Factor matrix (with VARIMAX rotation)

Factor loadings after rotation

Variable

Factor 1

Factor 2

Factor 3

Communality

Level of satisfaction with:

 

 

 

 

 

Wage level

0.27531

0.41617

0.32223

0.64718

Working hours

0.25667

0.76000

0.14035

0.33682

Working time flexibility

0.11844

0.65979

0.34775

0.42971

Resting time during working hours

0.19440

0.66019

0.27430

0.45113

Vacations and leaves

0.36796

0.60817

0.17563

0.46388

Job security

0.45680

0.44921

0.20555

0.54729

Interpersonal working environment

0.71652

0.21642

0.16747

0.41171

Job tasks

0.73742

0.20909

0.21816

0.36490

Physical environment

0.76017

0.18983

0.15177

0.36307

Health and safety at work

0.72832

0.23701

0.16336

0.38669

Training offered by the firm

0.33290

0.26599

0.59988

0.45857

Social benefits

0.01876

0.31362

0.73774

0.35704

Work and tasks organization

0.54969

0.24283

0.52426

0.36403

Autonomy

0.53520

0.14804

0.53909

0.40103

Participation in decisions

0.41563

0.05901

0.67675

0.36578

Promotion opportunities

0.20122

0.15486

0.76146

0.35570

Head’s assessment

0.56683

0.17349

0.51822

0.38005

Personal development at work

0.59733

0.11724

0.50624

0.37317

Free time

0.15596

0.60047

0.03905

0.61358

 

 

 

 

 

Eigen value

8.30885

1.43218

1.18764

 

% of explained variance

43.73%

7.54%

6.25%

 

% de explained variance, cumulated

43.73%

51.27%

57.52%

 

n= 1.378

 

 

 

 

Table 4. Principal Component analysis of satisfaction with different job aspects

 

Three factors emerged with Eigen values higher than 1. Satisfaction with wage, with job security, with work and task organization, with autonomy, with participation in decision-making, with managers’ assessments, and with personal development loaded with highly significant values on more than one factor, hence they could not be clearly assigned to any of them. The rest of the satisfaction variables were clearly assigned to one of the three factors. With the variables that clearly load on each factor, four new synthetic variables were created. They represent four dimensions of JS and have been labeled according to the type of initial variables they include. They are: satisfaction with time characteristics (Cronbach-alpha α = 0.77); satisfaction with contextual factors (α=0.81); satisfaction with non-financial compensation (α=0.76); and satisfaction with intrinsic factors (α= 0.83). Their composition is shown in Table 5. The first three are associated with extrinsic job factors. “Satisfaction with intrinsic job factors” is also included in the subsequent analysis and represents intrinsic job factors that can generate satisfaction (such as job autonomy and personal development). Also “satisfaction with wage” and “satisfaction with job security” are included. Ultimately, six dimensions of JS were considered in the subsequent analysis.

VARIABLES

 

Average (standard deviation)

 

 

n

 

Satisfaction with wage (0 -10)

6.17 (2.214)

1,632

 

Satisfaction with job security (0-10)

7.49 (2.374)

1,647

 

Satisfaction with time characteristics (0-10)

6.75 (1.788)

1,647

Satisfaction with working hours

7.03 (2.127)

1,647

Satisfaction with working time flexibility

6.46 (2.882)

1,674

Satisfaction with resting time during working hours

6.52 (2.650)

1,647

Satisfaction with vacation and leaves

7.37 (2.251)

1,647

Satisfaction with free time

6.41 (2.310)

1,647

 

Satisfaction with contextual factors (0-10)

7.49 (1.588)

1647

Satisfaction with interpersonal working environment

7.78 (2.001)

1,612

Satisfaction with job tasks

7.62 (1.838)

1,647

Satisfaction with physical environment

7.28 (1.980)

1,647

Satisfaction with health and safety

7.29 (2.065)

1,647

 

Satisfaction with non-financial compensation (0-10)

5.22 (2.766)

1,647

Satisfaction with training

5.92 (3.142)

1,647

Satisfaction with social benefits

4.48 (3.447)

1,465

Satisfaction with promotion opportunities

5.17 (3.210)

1,519

 

Satisfaction with intrinsic characteristics (0-10)

6.82 (2.020)

1,647

Satisfaction with autonomy

7.07 (2.436)

1,647

Satisfaction with participation in decisions

6.36 (2.863)

1,610

Satisfaction with head’s assessment

6.95 (2.440)

1,569

Satisfaction with personal development

7.16 (2.218)

1,647

Table 5. Descriptive statistics of job satisfaction dimensions

 

Table 5 shows that the sampled employees’ higher satisfaction levels were with job security, with the interpersonal environment and with job tasks. The lowest values were awarded to non-financial compensation; satisfaction with wage levels also shows low values. Hence, it seems that according to employees, Catalan jobs offer high job security and quite good contextual factors (such as health and safety) but low promotion opportunities, low training, low social benefits and low wages.

 

An OLS regression analysis was performed for each of the six dimensions of JS and overall satisfaction. Explanatory variables were the HRMP and control variables (firm and employee characteristics). Results are presented in Table 6. An OLS regression can be done with the synthetic variables generated for four of the satisfaction dimensions, since they have a large number of values, and hence can be considered quasi-continuous variables. An ordered model could have been used to estimate global JS and satisfaction with wage and job security, since satisfaction values reflect an order from 0 (none) to 10 (maximum). However, an OLS regression model was used to maintain model uniformity and simplify the analysis. According to Ferrer-i-Carbonell and Frijters (2004), proceeding in one way or the other (OLS or ordered models) does not seem to affect relevant results when analyzing the determinants of satisfaction, as we do here.

Explanatory variables (control variables)

Overall job satisfaction

Satisfaction with intrinsic factors

Satisfaction with contextual factors

Satisfaction

with time characteristics

Satisfaction with non-financial compensation

Satisfaction

with wage level

Satisfaction

with job security

 

CONTROL VARIABLES

Industry type (ref. services)

 

 

 

 

 

 

Primary Sector

-0.634

-0.040

-0.928*

-1.583**

-0.419

-0.878

-1.404*

Manufacturing

0.042

-0.140

0.055

-0.001

-0.034

0.085

-0.007

Construction

0.130

-0.040

0.263**

0.181

0.319

0.404*

0.182

Company size  (ref. Large)

 

 

 

 

 

 

Micro

0.268 *

0.485***

0.119

0.275*

0.001

0.391*

-0.052

Small

0.138

0.395***

-0.047

0.051

-0.020

0.331*

0.328**

Medium

0.065

0.006

-0.038

-0.015

-0.115

0.248

0.136

Private sector (ref. public sector)

-0.216**

-0.165

-0.091

-0.190*

-0.254

-0.062

-0.362**

Age (ref >55 years)

 

 

 

 

 

 

 

< 25 years

0.310

0.410**

0.210

0.046

1.111***

0.818***

0.660**

25- 44 years

-0.063

-0.057

-0.102

-0.048

0.369*

0.049

-0.092

45- 55 years

-0.114

-0.092

-0.083

-0.136

0.406**

-0.331*

-0.129

Gender: Woman (ref. man)

-0.108

-0.097

-0.091

-0.350***

-0.630***

-0.038

-0.086

Level of education (ref. university education)

 

 

 

 

 

No studies/ Primary education

0.397**

0.284

0.181

0.117

0.449*

0.397*

0.002

Secondary education

0.243*

0.203

0.142

0.042

0.271

-0.001

0.179

Professional education

0.237*

0.202

0.080

0.040

0.095

-0.048

0.207

Birth place (ref. Immigrant)

 

 

 

 

 

 

Catalonia

0.237*

0.276*

-0.085

0.043

-0.008

-0.089

0.130

Rest of Spain

0.188

0.428**

0.051

0.017

-0.088

-0.345*

0.367*

Professional category (ref. top manager)

 

 

 

 

 

 

Staff

-0.434

-0.600

-0.042

-0.743*

-0.761

-0.651

0.235

Manager (first level)

-0.171

-0.026

-0.038

-0.194

-0.308

-0.369

0.406

Manager (second level)

-0.255

0.121

-0.018

-0.130

-0.212

-0.433

0.583

Seniority in the firm (ref. > 20 years)

 

 

 

 

 

 

< 1 year

0.351**

0.332*

0.207

0.316*

0.477*

0.005

-0.208

1-2 years

0.196

0.023

0.047

-0.006

-0.011

0.015

-0.227

3-5 years

0.086

0.112

0.002

-0.033

0.049

-0.343

-0.223

6-10 years

0.275*

0.225

0.201

0.144

0.190

0.059

-0.286

11-20 years

0.050

-0.106

-0.060

0.061

0.123

-0.082

-0.308

 

CSR in HRM VARIABLES

Variable o mixed remuneration (ref. fixed)

0.001

0.049

-0.017

-0.155

-0.193

-0.080

-0.331**

No participation in profits (ref. Yes)

-0.104

-0.167

-0.099

-0.205*

-0.121

-0.126

-0.238*

Number of social benefits (between 0 and 10)

0.098***

0.049*

0.019

0.068***

0.241***

0.064**

0.084***

Monthly wage (ref. > 3.000€)

 

 

 

 

 

 

< 600€

-0.640*

-0.162

-0.054

-0.507

-0.285

-1.797***

-0.132

600-1.200€

-0.444

-0.229

0.069

0.002

0.602

-1.339***

-0.039

1.201-2.100€

-0.247

0.025

0.047

0.148

0.878

-0.452

0.079

2.101-3.000€

-0.502*

-0.276

-0.168

0.068

0.593

-0.197

0.060

Temporary contract

(ref. fixed)

-0.482***

-0.334**

-0.270**

-0.319**

-0.355*

-0.134

-2.096***

Part time work (ref. full time)

-0.263*

0.135

0.236*

-0.419***

-0.305

-0.083

-0.334*

No Training (ref. Yes)

-0.149*

0.105

-0.216***

-0.073

-0.816***

-0.256**

-0.186

Internal information

 (0=none; 10= much)

0.137***

0.229***

0.125***

0.106***

0.248***

0.110***

0.133***

No Team work (ref. Yes)

-0.322***

-0.408***

-0.269***

-0.253**

-0.557***

-0.182

0.019

Risk level  (0=none; 10=much)

-0.043***

-0.027*

-0.067***

-0.026*

-0.039*

-0.038**

-0.029*

Environmental workplace conditions (0=very bad; 10=very well)

0.084***

0.094***

0.146***

0.083***

0.159***

0.054**

0.079***

Safety provided by company

(0=never; 10=always)

0.091***

0.121***

0.105***

0.077***

0.119***

0.052***

0.066***

Level of discrimination and harassment (0=none; 10=much)

-0.185***

-0.252***

-0.160***

-0.137***

-0.124**

-0.214***

-0.179***

Maternity no harms the professional career (ref. Yes)

0.284***

0.334***

0.204***

0.355***

0.343**

0.209*

0.476***

Working longer hours

0=never; 10=always)

-0.008

0.005

-0.013

-0.052***

-0.008

-0.033

-0.010

Weekly hours at work

-0.026***

-0.005

-0.011*

-0.050***

-0.030***

-0.019**

-0.012

Difficulty getting leave

0=none;10= much)

-0.042***

-0.048***

-0.048***

-0.039***

-0.030

-0.021

-0.073***

Constant

7.615

3.417

5.681

8.879

5.904

6.018

8.925

 

 

 

 

N = 1.584

F = 10.26

R2 = 0.341

Adjusted R2 = 0.308

Adjusted R2 (only control v.) =0.088

N = 1.584

F = 17.79

R2 = 0.388

Adjusted R2 = 0.357

 

Adjusted R2 (only control v.) = 0.118

 

N = 1.584

F = 12.31

R2 = 0.383

Adjusted R2 = 0.352

Adjusted R2 (only control v.) = 0.071

 

N = 1.584

F = 8.78

R2 = 0.307

Adjusted R2 =0.272

 

Adjusted R2 (only control v.) = 0.065

N = 1.584

F = 11.06

R2 = 0.358

Adjusted R2 =0.326

 

Adjusted R2 (only control v.) = 0.096

N = 1.570

F = 6.05

R2 = 0.234

Adjusted R2 =0.196

 

Adjusted R2 (only control v.) = 0.079

N = 1.584

F = 11.38

R2 = 0.365

Adjusted R2 = 0.333

Adjusted R2 (only control v.) = 0.131

 

***p<0.01; **p<0.05; *p<0.10

Table 6. OLS estimation of job satisfaction

 

4.      Analysis of results 

Positive (negative) signs associated with explanatory variables indicate a positive (negative) effect of the variable on JS. For categorical variables (such as man/woman), results have to be interpreted in relation to the reference category (e.g. women are less satisfied -negative sign- with the time dimension than men). Given the large sample used for estimation, results are commented on considering that an effect is statistically significant only when p<0.01 or p<0.05. They are identified in the tables with two (**) or three (***) asterisks. It is also worth noting that with the regression analysis each variable’s effect on satisfaction is to be interpreted as “other things equal”, that is, the effect of other variables on satisfaction is already controlled for.

Due to the fact that the analysis contains many variables, both explanatory and dependent, many results emerge. For reasons of space, only some of them will be commented on in more detail below. Since the main focus of this research is the impact of CSR in HRMP practices on JS, we start by commenting on the results obtained for these variables. One of the main relevant results to consider is that all dimensions of CSR in HRMP are significant, either for overall satisfaction and/or for some dimensions of satisfaction. The direction of the effect is also as expected according to the first research hypothesis: practices in accordance with CSR have positive impacts on JS (such as good internal communication and information and non-discrimination and equal opportunities). Hypothesis two is also confirmed, as some practices are relevant to some satisfaction dimensions but not others (such as wage level, which is relevant for the dimension satisfaction with wage, but not statistically significant for overall satisfaction). Moreover, some practices have appeared as relevant for all or many satisfaction dimensions and also for overall JS (internal information and team work, health and safety at work, equal opportunities and non-discrimination practices, job security and working times and general practices for personal-work life conciliation).

 

Some of the specific results are as follows. With regard to wages, previous research offers non-unidirectional results, although most of these are in line with “higher wages increase JS” (as in Hamermesh, 2001; and for the case of Spain, Borra and Gómez, 2012; and Pouliakas and Theodossiou, 2010). In our analysis, the wage level effect is positive and statistically significant, but only for the dimension “satisfaction with wage level”. Therefore, it seems that higher wage levels increase satisfaction with it as a dimension, but do not seem to have any differential effect on overall satisfaction. Another HRMP which sample statistics showed to be important was job security. Regression estimates indicate that Catalan employees value this very highly, as it is statistically significant for nearly all dimensions of JS and for overall JS. Also, working part-time generally has a negative effect on JS. This may be an indicator that employees are not voluntarily deciding to work in part-time jobs and prefer full-time ones. With regard to training, in previous research it was found to increase satisfaction (Borra and Gómez. 2012; Edgar and Geare. 2005; Guest. 2002; Harley et al. 2007; Kucel and Vilalta-Bufí. 2013). Our results are similar: training increases JS with intrinsic factors and also with other dimensions of satisfaction such as with contextual factors, with non-financial compensation and with wage levels. Hence, it seems that employees perceive training as a means of somehow compensating other low-quality practices, such as low wages. With regard to internal information and communication, the previously reviewed literature showed that a positive effect was found in many studies. That is also the case here, notably being found to affect all dimensions of satisfaction and especially satisfaction with intrinsic factors (such as participation in decisions) and satisfaction with non-money rewards (such as promotion opportunities).

 

Health and safety at work increases JS, as found in Borra and Gómez (2012), Edgar and Geare (2005) and Sousa-Poza and Sousa-Poza (2000), and also has positive effects on all dimensions of JS. Non-discrimination and equal opportunities practices have the same effect, which is in line with the findings of Edgar and Geare (2005), Guest (2002) and Goldman et al., (2006), although descriptive statistics showed low levels of discrimination at work in our study. However, a high proportion of sampled employees (around 25%) said that maternity/paternity had harmed their professional career. Finally, with regard to practices related to working hours and personal/work life balance, our results show that these are generally relevant both to overall JS and the different dimensions of JS. Eurofound (2007) also found a general positive relationship between better practices and JS, such as in the case of greater working time flexibility and not working more than the normal number of hours. Previous research findings have also been in the same line (Borra and Gómez, 2012; Guest, 2002; Kucel and Vilalta-Bufí (2013); Macky and Boxall, 2007; 2008).

 

Results related to control variables are also worth commenting on, as they relate to the second research hypothesis. With regard to personal employee characteristics, being an immigrant, age and gender do not generally have significant effects. These results point to the first part of research hypothesis three: what really matters most are work practices rather than employees’ personal characteristics, in line with the main results obtained by Gamero (2010), Rico (2012) and Sánchez-Sallero et al., (2014). However, some personal characteristics are statistically significant for some JS dimensions in our regression analysis. In our case, although gender does not affect overall satisfaction (contrary to Rico, 2012), and as in Kucel and Vilalta-Bufí (2013), it does have a significant effect on satisfaction with time characteristics and with non-financial compensation, women being less satisfied than men. As for age, younger employees are more satisfied with some dimensions of JS. Concerning level of education, there seems to be some evidence that more educated employees are less satisfied. Summing up, results for employees’ personal characteristics suggest that even when relevant HRMP are included in the analysis, some personal characteristics do have an influence on JS.

 

5.      Discussion and conclusions

This research has analyzed the effects of a CSR approach to HRMP on Catalan employees’ JS, both as an overall measure and with regard to its different dimensions. As far as we know, this is a new contribution to the field, as there is no previous evidence available that is both broad enough and non-descriptive to make it useful for generalization and consequently usable for firms, trade unions, institutions and governments to implement actions towards increasing employees’ job quality and satisfaction with their jobs.

In accordance with most previous literature, we have found that better HRMP, orienting them towards what CSR defines as job quality, have positive effects on JS, whether overall or in some of its dimensions. Our first research hypothesis is therefore confirmed. However, some practices do not have effects on overall JS: training, wage level and any practice relating to remuneration in general. By contrast, and as regards the second research hypothesis, some practices are relevant to overall satisfaction and many dimensions of JS. This is the case with job security (having a permanent contract), which is the HRMP with the greatest effect on JS. It is also one that positively affects many dimensions of JS. The same is true of HRMP related to higher/better internal information and team work, health and safety at work, equal opportunities and non-discrimination practices and an adequate balance of work and personal life. For example, when employees find themselves in a good working environment (physically safe and with good interpersonal relations), their satisfaction with contextual factors (related to the working environment) increases, as does their satisfaction with other job dimensions such as wage level and job security. Therefore, these CSR measures are of great importance to firms interested in increasing their employees’ JS, since they affect many dimensions of satisfaction. They may also counteract the effects of practices which reduce job satisfaction, such as low wages.

We have also found that some practices only affect overall satisfaction or just one specific dimension of satisfaction. This is the case with wage level, which was found to have a relevant effect on the dimension “satisfaction with wage” (with lower-waged employees being less satisfied with their wages), but not on overall satisfaction. The type of monetary compensation (fixed or variable) and participating in benefits stand out as not having any effect on overall satisfaction, but are related to satisfaction with job security. Thus, Catalan employees do not see variable remuneration as a positive practice, as they associate it with job insecurity. This also happens with some practices related to working times and schedules, which only affect the dimension of satisfaction with time factors.

As for hypothesis three, some employees’ personal characteristics were found to be significant for JS. This knowledge provides us with information regarding which groups need specific attention. This is the case with women, who seem to value aspects of personal and working life balance higher than men, and with younger employees, who appear to be more satisfied with some dimensions of JS, all other things being equal. There is also some evidence that university-level employees may be less satisfied, other things being equal, than employees with lower levels of formal education.

These results are relevant for firms who have an interest in applying CSR in the management of their employees and increasing employees’ JS. They help identify which practices are more relevant to overall satisfaction, which are relevant to specific dimensions of JS, and which affect a broader range of satisfaction dimensions. They also help identify those groups of employees who may require specific practices (for example, women appeared to value time aspects of the job more highly, in line with job and personal life balance). 

Information obtained from this research may also be useful when defining institutional policies to promote job quality. This is the case with the social policy agenda of the European Commission and the Lisbon strategy of the European Council, which clearly link job quality with employees’ JS (COM (2001) 313 final). 

This research comes with limitations. It was conducted for only one of the seventeen autonomous regions in Spain. Other regions may have different economic structures   and employees may receive different job practices and feel differently from Catalan employees about job practices. Therefore, an analysis for other autonomous regions would provide broader information of potential differences among regions in Spain. Additionally, this research should be continued for other years in order to analyze similarities or differences in the results, especially considering the deep economic crisis enveloping Catalonia and Spain in general. The crisis has changed many job practices; for example, nowadays a far lower number of employees have a permanent contract, and permanent contracts also offer less job security than previously. Unfortunately, the QWL survey for Catalonia was disrupted some years ago, and no data are currently available to facilitate research from 2010 to date.  

In order to better understand the determinants of JS, future research should investigate the relationship between overall JS and its dimensions, and why some human resource practices have an impact on many dimensions and others only on a few. Moderating and mediating effects should also be explored; with job security, for example, which has emerged as being very relevant to both overall JS and its different dimensions.

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