Amsterdam Business School
Bachelor Future Planet Studies Major: Business Administration
The Influence of Technology Innovation on Job Satisfaction Which factors matter?
BSc Thesis by
L.M. de Vries 10793631
Supervisor: MSc.BA L.A. Napitupulu 27th June 2017
Statement of Originality
This document is written by Student Luc de Vries who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Table of contents
I. ABSTRACT ... 5 II. INTRODUCTION ... 6 III. THEORETICAL FRAMEWORK ... 8 TECHNOLOGICAL INNOVATION ... 8 JOB SATISFACTION ... 8 JOB INSECURITY ... 10 AGE AND GENDER ... 11 DOES SIZE MATTERS? ... 12 FUNCTION AND EDUCATION ... 13 IV. METHODOLOGY ... 14 RESEARCH SETTING ... 14 DESIGN ... 15 MEASUREMENTS ... 16 Independent variable: self-scanner availability ... 16 Dependent variable: job satisfaction ... 16 Mediator variable: job security ... 17 Moderator variables ... 17 PROCEDURE ... 18 ANALYSIS AND PREDICTIONS ... 19 The moderating effect ... 19 The mediating effect ... 20 V. RESULTS ... 22 CORRELATIONS AND RELIABILITIES ... 23 MAIN ANALYSIS ... 25 VI. DISCUSSION ... 28 SUMMARY ... 28 KEY FINDINGS ... 28 LIMITATIONS ... 29 CONTRIBUTION ... 30 VII. CONCLUDING THOUGHTS ... 31 VIII. REFERENCES ... 32IX. APPENDIX ... 36
1. THE SURVEY ... 36
I. Abstract
One can read in many scientific articles that technological innovations often alter the organisational structure. The main reason, stated by many, is the fact that dominant technology determines the way in which labour is divided. However, there is a general disagreement among researchers regarding the influence of technology on job satisfaction and job security. But there are some cases in which there is consensus: in the information industry, the link between the two is often made. This research aims to discover the influence of technology on job satisfaction and job security in the grocery store industry. A cross-sectional survey is distributed via social-media, among 4000 Albert Heijn employees to gather the data. With a response rate of 2.8%, 112 filled in questionnaires are collected. The overall results do not provide support for the theories. It turns out that in this sample, the self-scan system does not have a significant influence on job satisfaction, nor job security. However, job security does have a significant direct influence on job satisfaction (p = .004).
II. Introduction
Even though the word innovation seems to be relatively new, it originated already in the 16th century from the Latin words innovare, which means ‘renew’, and novus, which means ‘new’ (Oxford online dictionary, n.d.). The actual use of the word is also clearly visible over time. If we were to divide past decades into several innovation-related stages, we could do so in three, according to Fortnum (2016). The first stage would be the industrial revolution, which started around 1780. The development of steam and the use of mechanical production started. Many years later, around 1870, the so-called “2nd industrial revolution" took place, bringing the most significant innovation of that time, electricity. However, then, not even that long ago, the third and for now, latest stage started: the 1950s brought the transistor and thereby created the possibility to manufacture and use computers. The development of the transistor also paved the way for the research needed to create the world wide web eventually. Today, one can read everywhere about innovation or innovative companies. Forbes publishes a list each year of the most innovative companies. They need to meet several criteria, but most importantly, ‘the difference between their market capitalization and the net present value of cash flows from existing businesses' is used to predict their innovativeness (Dyer & Gregersen, 2016). Innovation is alive, it can be exciting to read about the most innovative companies, but is it all glamour?
Over half a century, the scientific debate around technological innovations and adoptions is still ongoing. The first thorough and far-reaching effects of these innovations were already described by Joseph Schumpeter in 1942 as creative destruction: "… incessantly revolutionises the economic structure from within, incessantly destroying the old one, incessantly creating a new one” (Swedberg, 1942/2013, p. 83). He describes the process of new technology adoption as one that is reforming the structure of a company from the inside and is thereby implicitly admitting that new technology adoption causes gains and losses. Years later, Solow (1957) proved, with the help of mathematical formulas, that the economic growth in developed countries is mostly attributable to technological innovations, and not the accumulation of capital. As a result of this Solow (1957) has proven how important technology is becoming.
Today, there are many sources that state something about the usefulness and effects of innovations within companies. Grant (2016, p. 170) describes innovation as one of the core drivers behind competitive advantage, and even sustainable competitive advantage if certain isolating mechanisms could guard it against imitation by other firms. This phenomenon is, according to Grant, the essence of the creative destruction definition by Schumpeter. So,
innovation is not only a direct force within a company, but is also disguised as other forces. Multiple authors, including for example Barney (1991) and Porter (1987), write about the importance of sustainable competitive advantage for the performance of an organisation.
Moreover, what is known about innovative technological implementations is that they alter organisational structure (Hickson (1969); Mirvis, Sales & Hackett, 1991). Often, new skills are required to learn to deal with the new technology, which causes the need to implement a training and support program. Also, Mirvis et al. (1991) state that employees form attitudes towards the new technology early on and that education is thus key. They believe that education can steer these beliefs to be beneficial for the company. Besides, techno-stress (Brod, in Mirvis et al., 1991) and pressure on the job (Mirvis et al., 1991) are existing and troubling consequences; supervisors expect more output after the new technology adoption, while employees not always experience the new technology adoption as productivity enhancing, which is causing pressure. To provide an overview and a useful framework in one, Orlikowski (1992) developed ‘the “technological imperative" model'. This model can be used to examine the impact of technology on organisational dimensions such as size, structure, performance, but also on individual dimensions such as job satisfaction, skill level, productivity and task complexity.
Continuing with the effects of innovation and technology, Shepard (1977) states that numerous researchers agree upon the fact that the dominant technology determines the way in which labour is divided. Also, the nature of the division of work is affecting other job characteristics. However, in contrast to this agreement, Shepard (1977) points out that researchers strongly disagree when it comes to the influence of technology on job satisfaction. Some believe that technology does not have that much of influence, while others believe it is of extreme importance when explaining variation in job satisfaction. Shepard (1977) also states that there is a gap in the imperative technological literature when it comes to the link between technology and work satisfaction. He indicates that, overall, imperative technological research aims to investigate work roles and the way work is designed. The missing piece in this genre of literature is to investigate whether employees benefit from technology innovations or not, regarding job satisfaction.
Therefore, the aim of this study will be to examine the relationship between new technology adoption and job satisfaction. The survey that is used in this research will focus on the general job satisfaction of the Albert Heijn employees, on general demographics, on their perception of the implementation and habituation towards the innovative process. The central
question that has to be answered by conducting this research is: what is the effect of a large-scale technology innovation on the job satisfaction of employees?
III. Theoretical Framework
Technological innovation
First of all, since this research is aimed towards finding a possible link between technology innovation of any kind and job satisfaction, technology innovation has to be defined. To start with, innovation is broadly used in academic literature. There is much variety when it comes to its definition, but the one of Damanpour (as cited in West & Altink, 1996, p. 4) is considered to be the best applicable here. According to him, technological innovations are those that: “… occur in the technical systems of an organisation and are directly related to the primary work activity of the organisation. A technological innovation can be the implementation of an idea for a new product or a new service or the introduction of new elements in an organisation's production or service operations…”.
Job satisfaction
First and foremost, job satisfaction has to be defined to understand the concepts of what is and what is not considered to be relevant for this research. Cranny, Smith and Stone (1992) state there is a consensus, in the form of a definition, when it comes to job satisfaction. Namely: "an affective (that is, emotional) reaction to one's job, resulting from the incumbent's comparison of actual outcomes with those that are desired (expected, deserved and so on)" (p. 1). This definition is in line with the definition of job satisfaction proposed by Locke (1969). Other authors, however, do not use the ‘affective' part in their definition of job satisfaction but are offering a definition which is about the attitude towards one’s job (Brief, 1998; Miner, 1992). Although these definitions seem slightly different (Weiss, 2002), the authors appear to neglect the possible inconsistency. For this research, there has been chosen to go with the definitions that incorporate the affective response or reaction to one’s job, mainly because the survey that is used builds upon it.
As Spector (1985) has already described, there is a wide variety of job satisfaction related research available, but there is a particular sector being left out in general. The human service industry and public organisations are, together with their employees, not often the central focus of this type of research. So, Spector developed the Job Satisfaction Survey (JSS), specifically suited for this kind of industry. Often in literature, job satisfaction is concerned
with the emotional-affective response to the job as a whole or certain facets of a job (Locke, in Spector, 1985; Smith, in Spector, 1985). Spector states that in his research the JSS was designed in such a way that it can measure job satisfaction as a combination of evaluative feelings about a job, which enables the JSS to measure overall job satisfaction since “… there is considerable empirical evidence that a linear combination of satisfaction aspects is an adequate overall satisfaction measure” (Spector, 1985, p. 695).
From the sectors where job satisfaction research is indeed readily available, like the information industry, the link between job satisfaction and technology innovation and implementation is often made. Described by Orlikowski (1992), it seems the case that job satisfaction even can be manipulated by optimising the so-called Socio-Technical factors of a job. Established by Trist, Higgin, Murray and Pollock (1963), the Socio-Technical Approach aims to assign equal weights to social and technological issues, when it comes to the analysis of a system that is going to be implemented. This approach recognises that a system entails both technical aspects and social impacts on the organisation and its workforce.
Looking at the several authors who conducted research aimed at discovering a link between new technology and employee job or work satisfaction, it becomes apparent that the scientific world has not reached a consensus yet. Based on the work of Mirvis et al. (1992), mainly focussing on techno-stress and pressure on the job, there is a reason to believe that new technology implementation could cause a decrease in job satisfaction. However, why there is not yet a consensus becomes clear when one studies the work done by Shepard (1977). He states that many researchers do not use technology as an independent variable, but are using job characteristics that are associated with specific technologies instead. In general, worker control, social interaction on the job, job specialisation and skill level are used as the main job characteristics. From this early work, done by Shepard (1977), one can learn two things: the consensus cannot be reached because of the inconsistencies between researchers concerning job characteristics, and, the influence of technology on job satisfaction significantly depends on the type of technology that is being examined.
Based on the literature, hypothesis 1a is formed:
Job insecurity
Many researchers have provided us with a definition of job insecurity. According to Probst (2003), Greenhalgh and Rosenblatt (1984) were among the first researchers to formulate a definition of the job insecurity phenomenon. They defined it as “perceived powerlessness to maintain desired continuity in a threatened job situation” (Greenhalgh & Rosenblatt, 1984, p. 438). However, others believe that perceived powerlessness is more likely to be a moderator of job insecurity and shape the definition of job security as the perceived stability and continuance of one’s job as one knows it (Probst, 2002).
Since this research is specifically focused on the influence of technology innovation, the definition of Greenhalgh and Rosenblatt (1984) seems to fit best. The perceived powerlessness is anticipated to be caused by new technology implementation since there is nothing the employees can do about it. Once corporate headquarters decide on implementing a new system, their employees just have to live with it. This feeling can cause perceived powerlessness and is therefore considered to be important to take into account. Although the definition stated above fits well, it should be elaborated by adding something about the expectations one has about his or her job. As mentioned in earlier research, an employee’s “expectation about continuity…” is also an important part of job security (Davy, Kinicki & Scheck, 1997, p. 323). That is, in this research, job insecurity will be defined as the perceived powerlessness to maintain, and the expectations about continuity, in a threatened job situation.
Furthermore, as stated by Ashford, Lee and Bobko (1989), organisational change can form a threat to employees' sense of control. Moreover, they propose technology innovation as an example of organisational change, and state that it is likely to alter or eliminate jobs in general, which causes, in turn, a decrease in job security. Important to note here is that inaccurate information about the consequences of organisational change often circulates among employees, causing them to experience unwarranted insecurity (Schweiger & Ivancevich, 1985). Therefore, based on this earlier research, hypothesis 1b is formed:
H1b: The availability of the self-scan system is negatively related to job security Some previous research has already examined the possible link between job security and job satisfaction, but often in a slightly different scientific setting. Among those researchers are Oldham, Kulik, Ambrose, Stepina and Brand (1986). Their research proved that the group of employees with a lower perception of job security experienced an overall lower job satisfaction. However, the scales that they used to measure job security differ on several points
from for example the scale developed by Ashford et al. (1989). The main difference between the two is that the latter focusses a greater deal on the different facets of job security, and combining them into one scale. Furthermore, job insecurity is linked with the negative affective responses to a threatened job situation. That is why Ashford et al. (1989) were able to prove that it is indeed likely that job insecurity has a negative impact on job satisfaction since the latter is concerned with an affective response to a job.
However, this could in turn also entail that when employees do not experience a decreased sense of job security, due to the implementation of a new technological system, job satisfaction is in its turn also not affected. When, for example, the position of an employee is enhanced by the implementation, because he is the leading expert when it comes to the use of this new technology, it could well be that job security even enhances, and so does job satisfaction. Building upon previous research, hypothesis 2a and 2b are formed:
H2a: Job security is positively related to job satisfaction
H2b: Job security positively mediates the relationship between the availability of the self-scan system and job satisfaction
Age and gender
When it comes to age and gender, there is a sufficient amount and a wide variety of research available to build an extensive theoretical base. Morris, Venkatesh, and Ackerman (2005) for example, examined the moderation effect of age and gender combined. Among other things, they found that gender differences in adopting new technology were more extent with increasing age. Li, Glass, and Records (2008) have proven that there are indeed gender differences and found that their male respondents move more quickly through the new technology adoption phase than female respondents. More in-depth age research has been done by Morris and Venkatesh (2000). They examined whether age differences matter when it comes to new technology adoption and sustained usage. For their research, they used the theory of planned behaviour, which will be excluded in this study since it is too in-depth and age is not considered to be the most important moderator. Moreover, Morris and Venkatesh (2000) conclude that age does matter both from a statistical and societal point of view. Life expectations are rising, and people do need to keep working for a longer part of their life, so understanding the relationship between age and technology adoption is of great importance. Since there is extensive literature readily available on this topic, the aim of this specific
sub-question is not to discover a new link, but more to check if this population behaves like the ones described in the other studies. So, based on this literature, the following hypotheses are formed:
H3a: The effect of the implementation of the self-scan system on job security will be less strong for male respondents.
H3b: Age will negatively moderate the relationship between the self-scan availability and job security
Does size matters?
There is also a possibility that the scale of the company matters regarding the influence of new technology on job satisfaction and overall job performance. Of the four types of organisational structures, described by Miles, Snow, Meyer and Coleman (1978), the ‘defender' and ‘prospector' are of interest here. Namely, the defender chooses a stable and narrow approach and mainly uses one technology. On the contrary, the prospector does the opposite; multiple and flexible technologies and exploiting new product and market opportunities (Miles et al., 1978). The research of Laforet (2007) builds upon these structures by stating that prospectors are in general larger companies than defenders. So, this would mean that size does matter. However, what about when a small, defender company decides that it must innovate? Most likely, the employees do not react in the same manner as the employees from the larger, prospector companies, who are used to innovating processes. This is what habituation is all about. If an employee works at a store that is often used as a pilot store for new concepts, the adoption of the self-scan system will have less impact on those particular employees.
Moreover, an innovation of technological nature of a similar size does have a bigger impact on a small business than it does on a large one. The research conducted by Hickson, Pugh and Pheysey (1969) showed that the smaller the organisation, the greater the effect of technology innovation on organisational structure in general. The authors attribute this finding to the fact that in smaller organisations everyone is closer to the "shop-floor"; hence everyone has to deal with the new technology directly. In line with this explanation, the fact lays that in larger organisations management, administrative and several other departments are shielded from the technology itself by a separate technology department. These findings and explanations are in line with the research of many others (Blau, McHugh-Falbe, McKinley and Phelps (1976); Carter (1984); Orlikowski (1992).
H4a: Store size will positively moderate the relationship between the self-scan availability and job security
H4b: Habituation will positively moderate the relationship between self-scan availability and job security
Function and education
There is enough scientific evidence to think that job security is influenced by the function the employee has in the organisation, especially when a clear distinction can be made concerning hierarchy and perceived prestige. When an employee gets a promotion and is promoted to a higher position regarding perceived prestige, his job security increases. Often, the assumption is made that a promotion entails a raise and is caused by some skill an employee possesses over his colleagues. This assumption justifies the theory about the influence the position an employee has within an organisation and his job security. Namely, because Frese (1985) proves that there is a correlation between salary and the skill level of employees and job security. Lower skilled and less paid employees encounter a greater sense of insecurity according to his research. Also, research conducted by Barlin and Kelloway (1996) shows that the amount of control an employee has over decision-making within the company can positively affect his job security. This is a relative finding since it is reasonable to assume that promotion often goes, in general, hand in hand with a stronger say in decision-making.
H5: Function will positively moderate the relationship between the self-scan availability and job security
Moreover, the level of education of employees is often a moderator for job security. Many researchers write about the influence that education has (Clark & Postel-Vinay, 2009; Frese, 1985; Oshagbemi, 1997; Yousef, 1998). As described by Clark and Postel-Vinay (2009), lower educated employees feel somewhat less secure in their job. As a reason, the authors propose that the lower-skilled side of the labour market encounters in general less favourable conditions. Also, higher education is often related to promotion, meaning that in this case higher education also influences job security via the employees’ function described above.
H6: Education will positively moderate the relationship between the self-scan availability and job security
Figure 1, displayed below, provides a graphical overview of the complete conceptual framework used in this research.
IV. Methodology
Research setting
The adoption of the self-scan system by Albert Heijn is, by definition provided in the previous chapter, and thus by this paper considered to be a technological innovation; it has occurred in the technical system of the Albert Heijn, and it is both a new product as a new service. After labelling the self-scan system as an innovation of technological nature, it can be useful to take a look at its potential rate of diffusion. In other words, how fast is the system going to "spread" across the stores of Albert Heijn? Rogers (2010) has come up with five characteristics, relative advantage, compatibility, complexity, observability, trialability, which could provide an overview of the extent to which customers are going to accept new technology and thus could indicate the rate of diffusion. If these characteristics are applied to the Albert Heijn self-scan system, it becomes apparent that: relative advantage is potentially significant, compatibility is extensive since the system is considered to be socially accepted, complexity is seen as low because the system is easy to use, observability is moderate, and trialability is not really important in this case since Albert Heijn already has the system in place in 220 stores.
Design
The survey used in this research (Appendix 1) is cross-sectional, meaning that it gathers data at one particular point in time. By utilising this type of survey, the option of finding a sequence of events has evaporated. Since Albert Heijn is considered to be located for a large part in the human service industry, the JSS developed by Spector (1985) can be utilised. However, since this research is aiming at investigating a possible new relationship between a great technology innovation and job satisfaction, trying to discover a sequence is not yet necessary (Johnson & Hall, 1988). First, the question if the relationship even exists has to be answered. In this research, around 4000 employees of Albert Heijn were initially targeted via social media. There are no specific requirements respondents have to meet to participate, except working at the Albert Heijn. Below, some general statistics concerning the sample of 112 respondents are displayed in Table 1.
Moreover, a quantitative approach has been chosen; employees of Albert Heijn will be questioned about the self-scan system that is being used in 220 stores. This system is relatively new and is available for customers in about 26% of the stores (Albert Heijn, n.d.). It enables customers to skip the traditional cash register by letting them scan products while they are shopping, check out when they are done and pay at one of the unmanned cash registers located at the end of the store, next to the "conventional cash registers". By implementing this system, Albert Heijn provides its customers with a convenient, fun and quick way of doing groceries, but what about the employees? Earlier mentioned creative destruction suggests that the "old structure" is being destroyed. For example, regular cash register employees see fewer people, and the probability of needing fewer people for the cash register job, in general, is quite large since the self-scan cash registers only employ one employee for all registers, while it is one on one in the regular system.
Table 1. General statistics Variable Respondents Gender Male 42 Female 69 Missing 1 Age <18 12 18-25 85 26-30 9 31-40 1 41-50 1 51-60 4 >60 0 Missing 0 Education WO 9 HBO 29 MBO 8 High school 55 Other 11 Missing 0
Type of employment Full-time 14
Part-time 60%-99% 22 Part-time <60% 76
Missing 0
Self-Scan availability Yes 45
No 67
Missing 2
Note: WO = university, HBO = university of applied sciences, MBO = intermediate vocational education
Measurements
Independent variable: self-scanner availability
Whether the self-scanner system is available in the Albert Heijn store where the respondent is working is simply measured by proposing the question: Does the Albert Heijn where you are employed has the self-scanner system implemented? The possible answers are ‘yes’ or ‘no’. Since the availability is measured with a single question, a reliability analysis is not executed.
Dependent variable: job satisfaction
The dependent variable job satisfaction is measured with the help of the JSS, composed by Spector (1985). The original JSS incorporates 36 items, measuring job satisfaction on nine different aspects. For this research, the JSS is abridged to 16 items, because, considering the
overall estimated completion time of the survey would be too long, risking loosing respondents in the process. The original JSS features numerous, self-rated, questions that measure approximately the same aspect, for example, I like the people I work with, and, I enjoy my co-workers. To reduce the chance of losing valuable information while cutting down on the number of questions, only one of each of these "duplicate questions" is discarded.
As drafted by Spector (1985), the modified JSS measures the variable on a 16 item Likert scale ranging from a minimum of 1 (completely disagree) to 6 (completely agree). The scale is unipolar and therefore does not includes a neutral value. An example of an item on this scale is: I believe I am getting paid enough for the work I do. If a respondent scores low on this part, it means that he experiences a relatively low job satisfaction. Since low scores are linked to a low job satisfaction, the questions that are formulated negatively need to be reversed scored. For example: Sometimes I feel that my job is meaningless, needs to be reversed scored so that the lowest value is corresponding with completely agree. This way, scoring low on this part still means an overall lower job satisfaction. The reliability analysis provided a Cronbach’s α of .725.
Mediator variable: job security
With the use of the Job Insecurity Scale (JIS), composed by Ashford, Lee and Bobko (1989), the mediating variable job security is measured. The original JIS incorporates 57 items, but again due to the necessity of limiting the overall completion time it has been slimmed down to 8 items. From a theoretical point of view, the questions that were picked are those that focus on the perceived threat to the total job. The modified JIS measures the variable on an 8 item Likert scale ranging from a minimum of 1 (very unlikely) to a maximum of 5 (very likely). This scale is considered to be bipolar since it contains a neutral (3) value. The respondents are asked to think about the future and estimate the chance that event x will happen. An example of an event x is: Lose your job and be moved to a lower level job within the organisation. Scoring overall low values on this part translates into a relatively low job insecurity, which means a high job security. The reliability analysis provided a Cronbach’s α of .605.
Moderator variables
Gender is considered to be one of the moderator variables, and is being measured with the question: how do you identify? The possible answers are: ‘man’ or ‘woman’. For moderator variable age, the question is: what is your age category? The answers are in the form of categories: <18, 18-25, …, 51-60, >60. The next variable, educational level, is measured by
posing the question: what is the highest educational level you have finished? The possible answers are: ‘WO’, ‘HBO’, ‘MBO’, ‘high school’ and ‘other’. Furthermore, the function of the respondent is measured by posing the question: what is your function? The possible answers are: ‘manager’, ‘team leader’, ‘re-stocker of shelves’, ‘self-scan employee’, ‘cash register employee’ and ‘different’. Next is the moderator that measures in what kind of Albert Heijn store the respondent is working. This measure is also automatically indicating the store size. The question posed here, is: at what type of Albert Heijn are you employed? Also, a link to a page of the Albert Heijn website about the different types of stores is provided, in case respondents do not recognise the type of store they work at. The possible answers are: ‘regular Albert Heijn (wijkwinkel)’, ‘Albert Heijn to go’ and ‘Albert Heijn XL’.
The variable habituation is measured as a combination of 2 variables. First of all, the attitude of the respondent towards technology innovation, in general, is measured by posing the question: how do you feel about technology innovations in general? The possible answers range from 1 (very positive) to 5 (very negative), with 3 (neutral). Second, the frequency with which new concepts or ideas are tested in the respondent’s store is measured by asking the question: some Albert Heijn stores are more often being used to test out new concepts or ideas than others. How would you rate the amount of innovative concepts or ideas that are being tested as a pilot in your store? The possible answers range from 1 (never) to 4 (always). All of these variables are measured with a single question. Hence a reliability analysis is not performed.
The entire operationalized framework, including the key variables, their measurements and hypotheses, is visualised below in figure 2.
Procedure
The respondents were contacted via a post in a Facebook group of Albert Heijn employees. This group has a little over 4000 members; the exact number of members fluctuates sharply over time. The post contained general information about the survey, such as the estimated duration, the guaranteed anonymity and an anonymous link to the online survey. With 112 respondents, the response rate is 2.8%.
Analysis and Predictions
For both the moderating as the mediating effect within the conceptual model (figure 1), the steps that are taken are based on the steps Baron and Kenny (1986) describe in their work. These steps are considered to be part of the right method to examine the relationships outlined in this research.
The moderating effect
Starting with the moderating part of the conceptual model (figure 1), the first step of the analysis is displayed in figure 3. This step entails the regression analyses, which aims to predict the direct effects of both the independent and the moderator variables, on the independent variable. Relying on the hypotheses, a direct negative effect of the self-scan availability on job security is expected, meaning that the availability will decrease job security. Secondly, for moderating variable age, a direct negative effect is expected; with increasing age, job security is declining. Thirdly, concerning gender a direct negative effect is expected to be found; female respondents will encounter a lower job security.
On the contrary, a positive direct effect is predicted for moderating variables education, function, store size and habituation. Firstly, for education, this would translate into a higher job security when the respondent is higher educated. Secondly, in terms of function, this would entail a higher job security when the respondent finds himself situated in a higher position within the organisation. Thirdly, for store size, it would mean with increasing store size, job
security increases as well. Also, lastly, for habituation, it means that respondents who are more habituated experience a higher job security.
Continuing with the second step of the analysis, displayed in figure 4, the interaction effect of each moderator variable is examined by performing a regression analysis for each of the moderator variables. Based on previously stated hypotheses, significant Pearson Correlations for each of these variables are expected to be found. Moreover, there is expected that age and gender will negatively moderate the correlation between the self-scan availability and job security. Furthermore, moderating variables education, function, store size and habituation are all expected to positively moderate the correlation between self-scan availability and job security.
The mediating effect
To investigate the mediating effect within the conceptual model, Baron and Kenny (1986) instruct four steps. Each step entails a regression analysis. The first step is visualised in figure 5 and aims to find a significant effect between the independent and dependent variable. In this case, the first step examines whether the self-scan availability is significantly correlated with
Figure 3. The first step of the moderating part
job satisfaction. Expected is that this is indeed the case and also that the direct effect will be negative; the availability will lead to decreased job satisfaction. This expectation is based on the work done by Shepard (1977), as described in the previous chapter. The impact of the technology will flow mainly through other, earlier mentioned, job characteristics. In this case, worker control is somewhat influenced since cash register employees who are being transferred to the self-scan system do have less control. The social interaction on the job is most certainly decreased. A self-scan employee sees a lot fewer people in person, compared to a conventional cash register employee, who speaks to every customer that passes by. Furthermore, skill level and job specialisation do not change significantly.
The second step, visualised in figure 6, aims to find a significant correlation between the independent variable and the mediating variable. In this case between self-scan availability and job security. Again, a significant negative correlation is expected to be found; the availability will decrease job security.
The third step (figure 7) aims to find a significant correlation between the mediating variable and the dependent variable. In this case, between job security and job satisfaction. It is is expected to find a significant, and positive correlation; a high job security predicts a high job satisfaction.
Figure 5. The first step of the mediating part
The final step aims to find that the effect of the independent variable on the dependent variable is less strong or not significant when the mediating variable is considered. Now the whole mediating model (figure 8) is tested. In words, the direct effect of the self-scan availability on job satisfaction is less strong compared to the effect through mediator variable job security.
V. Results
For this part, there is chosen to divide the research between models, according to the step described above. Model 1 refers to the first step of the moderating part, incorporating the direct effects of the independent and moderating variables on the dependent variable. Model 2 relates to the second step of the moderating part, namely the interaction effect of each of the moderating variables. Model 3 refers to the first three steps of the mediating part, hence including the direct effect of the independent variable on both the mediating and de dependent variable and the immediate effect of the mediating variable on the dependent variable. Model 4 refers to the fourth step of mediating part, namely the complete mediation effect.
Figure 7. The third step of the mediating part
Correlations and reliabilities
For all variables, the means, standard deviations, correlations and reliabilities are displayed in Table 2. The Cronbach's α for both the JSS as the JIS are satisfactory (Field, 2009). The α of the JSS could have been improved slightly with .01 by deleting the item many of our rules and procedures make doing a good job difficult, but from a theoretical point of view, it is not desirable to do so since this item measures valuable information. Also, the improvement of .01 does not weigh out against the loss of information.
As forecasted before, the moderating variable age is indeed negatively correlated to job security, r (112) = -.267, p = .007. Unexpectedly, the other variables are not found to be correlated to job security. Their r and p values can be read from Table 2.
Furthermore, for model 3 goes that the independent variable self-scan is, unfortunately, not significantly correlated to dependent variable job satisfaction, r (112) = .014, ns. The independent variable also proves not to be significantly correlated to the mediating variable JIS, r (112) = -.118, ns. But, as expected, the mediating variable job security does significantly correlate to the dependent variable job satisfaction, r (112) = -.285, p = .004.
Table 2. Descriptive statistics Variable M SD 1 2 3 4 5 6 7 8 9 1. Gendera 1.62 .487 2. Age 2.16 .926 .056 3. Education 3.27 1.185 .009 -.196* 4. Function 2.83 2.089 .227* -.003 .033 5. Self-scan availability 1.60 .492 -.025 -.035 .186* -.068 6. Type of store 1.14 .500 .002 .125 -.004 -.028 -.241* 7. Habituation 2.44 .599 .209* .001 .181 .028 .378** -.092 8. JSS 3.77 .652 -.089 .074 -.009 -.247* .014 .073 -.202* (.725) 9. JIS 2.10 .530 -.070 -.267** .088 .049 -.118 -.031 -.003 -.285** (.605)
**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).
a 1 = male, 2 = female Note: Cronbach's ⍺ between parenthesis
Main analysis
This section will provide an overview of the results from the main analysis. The main analysis involves the four models as described above. According to Field (2009), using regression analyses is the appropriate form of analysing. Combined with the outlines set by Baron and Kenny (1986), the analyses needed for each model are preformed, and the results are displayed in Table 3 and 4.
Firstly, unexpectedly, the results show no support for the correlation between the availability of the self-scan system and job satisfaction, nor job security (β = .014, ns, R2 = .00 and β = -.118, ns, R2 = .061 respectively). Hence, these results do not support Hypothesis 1a: the availability of the self-scan system is negatively related to job satisfaction, and 1b: the availability of the self-scan system is negatively related to job security. These results imply that, for this sample, the availability of the self-scan system did not have any influence on job satisfaction and job security.
Secondly, as expected, the results prove that there is indeed a correlation between job security and job satisfaction (β = -.288, p = .004, R2 = .082). Hence, the results support
Hypothesis 2: job security is positively related to job satisfaction. In other words, employees who encounter a strong feeling of job security also encounter a strong feeling of job satisfaction.
Thirdly, again unexpectedly, the results do not show significant support for the moderating effects of gender, nor do they support the moderating effect of age, on job security (β = -.017, ns, R2 = .120 and β = -.197, ns, R2 = .120 respectively). Hence, these results do not support Hypothesis 3a: the effect of the implementation of the self-scan system on job satisfaction will be less strong for male respondents, and 3b: age will negatively moderate the relationship between the self-scan availability and job security. This implicates that the effect of the self-scan availability on job security does not differ between men or women. Also, it implicates that the age of an employee does not influence the impact the availability of the self-scan system has on their job security.
Fourthly, also not as expected, the results do not show significant support for the moderating effects of store size and habituation on job security (β = .096, ns, R2 = .120 and β = .156, ns, R2 = .120 respectively). Hence, these results do not provide support for Hypothesis 4a: store size will positively moderate the relationship between the self-scan availability and job security, and 4b: habituation will positively moderate the relationship between self-scan availability and job security. This means that there is no difference between small or large stores, regarding the effect that the availability of the self-scan system has on job security. Also,
the amount of habituation an employee experiences does not influence the effect of the self-scan availability on job security.
Fifthly, unexpectedly, the results do not show significant support for the moderating effect of function on job security (β = .052, ns, R2 = .120). Hence, these results do not provide support in favour of hypothesis 5: function will positively moderate the relationship between the self-scan availability and job security. This implicates that there is no difference concerning the effect of the self-scan availability, among the several functions that an employee can be in. Lastly, and again unexpectedly, the results do not show significant support for the moderating effect of education on job security (β = .050, ns, R2 = .120). Hence, these results do not provide support for Hypothesis 6: education will positively moderate the relationship between the self-scan availability and job security. This means that the effect of the self-scan availability on job security is not different for the different educational levels of the employees.
Table 3. Regression results of the moderating part
Job security (DV) Model 1 Model 2
Coefficient SE Beta Coefficient SE Beta
Constant 2.184 .407 2.067 .064 SCA -.126 .106 -.118 -.079 .133 -.076 Gender -.071 .115 -.066 -.019 .124 -.017 Age -.093 .066 -.169 -.109 .070 -.197 Education -.004 .046 -.009 .009 .047 .021 Function .004 .026 .018 .001 .028 .005 Store size -.010 -.010 -.010 .022 .161 .023 Habituation .026 .091 .030 .021 .106 .025 SCA*Gender .034 .255 -.053 SCA*Age -.116 .141 .031 SCA*Education .045 .096 .050 SCA*Function .026 .057 .052 SCA*Store size .169 .284 .096 SCA*Habituation .307 .229 .156 R^2 .061 .120
Table 4. Regression results of the mediating part
Model 3 Model 4
Step 1 Step 2 Step 3 Step 4
Dependent variable Job satisfaction Job security satisfaction Job satisfaction Job
Coefficient SE Beta Coefficient SE Beta Coefficient SE Beta Coefficient SE Beta
Constant 3.745 .218 2.297 .176 4.511 .257 4.559 .346 SCA .018 .132 .014 -.126 .106 -.118 -.027 .128 -.020 Job security -.351 .119 -.285 -.354** .120 -.288 R^2 .000 .014 .081 .082 Note: N = 112, **p<.01
VI. Discussion
Summary
The main goal of this research is to examine the influence of technological innovations within companies on the job satisfaction of employees. Moreover, there has been looked at the possible effect of job security, on the relationship between a technological innovation and job satisfaction. The research question that has to be answered is: what is the effect of a large-scale technology innovation on the job satisfaction of employees? Also, several factors such as age, gender, store size, and more are tested to see if they alter the relationship between new technology implementation and job security.
Key findings
Overall, the results were mostly unexpected. Firstly, hypothesis 1a stated that the implementation of the self-scan system has a negative effect on job satisfaction. Based on this sample, it turned out that this was not true. There is no reason to believe that the implementation had any effect on job satisfaction. In other words, employees who work in a store that has the self-scan system in place, do not experience lower job satisfaction than the others. Secondly, hypothesis 1b stated that the availability of the self-scan system has a negative impact on job security. This was also not supported, which means that employees who work at an Albert Heijn store that has the system in place, do not encounter a lower job security compared to the employees who work in a store where the system is not implemented. Thirdly, hypothesis 2a stated that job security has a positive effect on job satisfaction. This is indeed supported, meaning that employees who encounter a greater feeling of job security, also experience a greater sense of job satisfaction. Fourthly, hypothesis 2b states that job security has a positive influence on the relationship between the self-scan system and job satisfaction. This was unexpectedly found not valid, meaning that the effect of the self-scan system on job satisfaction is not less negative when employees experience a greater feeling of job security. From the earlier results it was expected however, since the direct effects of the self-scan system on both job satisfaction and job security was not present
Furthermore, hypothesis 3a stated that gender is of influence on the relationship between the self-scan system and job security. This is however untrue; the effect does not differ for male or female employees. Hypothesis 3b stated that age has a negative effect on the relationship between the self-scan system and job security. This is also untrue; the different age categories do not react differently to the self-scan system regarding job security.
Moreover, hypothesis 4a stated that the store size is of influence on the relationship between the self-scan system and job security. This is however also untrue; working in a larger store does not alter the effect the self-scan system has on job security, nor does working in a smaller store. Also, hypothesis 4b, which stated that habituation would positively influence the relationship between the self-scan system and job security is untrue. This means that the relationship is not altered when employees encounter a larger habituation towards technological aspects of their lives.
Besides, Hypothesis 5, which stated that function would positively influence the relationship between the self-scan system and job security if also found to be untrue. This means that the relationship is not altered if the different functions that employees could possess are taken into the equation. And finally, Hypothesis 6, which stated that education would have a positive influence on the relationship between the self-scan system and job security is found to be untrue. In other words, there is no difference between higher or lower educated employees concerning the effect of the self-scan system on job security.
Limitations
The first and foremost limitation of this research is attributed to the fact that the sample is heavily skewed, age wise. 86.6% of the respondents are younger than 25 years old (see Appendix 2 for a visualization, skew: 2.795). Besides a N that is large enough, another important criterion for a statistical analysis to be able to say something useful about other cases is that the sample being used is normally distributed (Field, 2009). In this case, the sample is not. Not only is it skewed in terms of age, but also regarding employment (67.9% works part-time <60%, skew: -1.277). The explanation for this skewness lies in the fact that the data is gathered through the use of social media. Readily available is scientific literature that has aimed to investigate the demographic of social media users, and basically, they agree almost always on one fact: there are relatively more young people using social media (Duggan & Brenner, 2013; Correa, Hinsley & Zuniga, 2010). Also, this skewness in age is believed to be the cause of the skewness in employment as well. In this research, age and employment were significantly correlated, which seems logical since a lot of young people have side jobs like working at grocery stores, in this case, Albert Heijn. Because they work at the Albert Heijn as a side job, they will work fewer hours compared to people who work there to provide for their living.
These findings are causing two main thoughts. The first thought is about a possible correlation between age and carelessness. It is most certainly not unthinkable that younger, part-time working employees do not care about their job security and, thus, job satisfaction. When
working a side job, while for example studying fulltime, the chances are that one does not care about holding their job for a long time. The barriers to switching jobs are relatively low when a job is on the side, compared to someone who needs the job fulltime to provide for his living. A side note to this remains still that it seems strongly dependent on the sector and on the fact that the job is indeed seen as a side job. There are namely enough scientific articles about young professionals and their commitment (Kaldenberg, Becker & Zvonkovic, 1995). But most of the time, this is about relatively higher educated employees, in completely different sectors than the grocery stores. The second thought is about this research in particular. There is limited data available about the demographics of grocery store employees in general, but what are available points to a sort of general skewness in the industry. The U.S. bureau of labor statistics (n.d.) states that over a third of the jobs in the grocery stores industry is hold by employees ageing from 16 to 24. In short, the skewness of the sample concerning age does not have to necessarily be a problem when this research is utilised for this particular industry. It does, however, when one tries to say something about other sectors.
Some more food for thought arises out of the possibility that young people are far more used to technology compared to elderly people. The young people of today grew up with technology and its innovations happening from day to day. For the elderly of today, a lot of them see it still as something more revolutionary, and therefore experience a more difficult time adopting. As described by Morris and Venkatesh (2000), two different age groups do react differently on new technology implementation. Also, the younger groups are more easily adopted to the new systems. This could mean that for younger groups, technology innovation does have less effect on their job satisfaction because of this.
Another point of concern is the Cronbach’s α of the JIS (.605). It is acceptable but at the very low spectrum of the ‘acceptable’ range. For example, Field (2009) states that a scale is reliable from values ranging between .7 and .99. This low Cronbach's α, compared to the original α from the JIS developed by Ashford et al. (1989), is probably due to the fact that is was slimmed down for this research. While doing so, it could be the case that valuable information is lost.
Contribution
The first and foremost outstanding contribution is the fact that this research shows that employees do not always fear technological innovations. This sample shows support for the thought that new technology does not necessarily have to have an impact on employees, in
terms of job security and satisfaction. Moreover, this research provides more prove for the correlation between job security and job satisfaction. This adds to existing research by providing yet another confirmation, this time from the grocery store industry. Further, this research contributes to possible future research by providing a base to examine, for example, a possible correlation between age and carelessness, in terms of job security and satisfaction.
VII. Concluding thoughts
While in some industries, for example the information industry, researchers do have reached some kind of consensus about the influence of technology on job satisfaction, it appears not applicable in this case. This research is not able to link the job satisfaction of the employees of Albert Heijn to the recent technological innovation that occurred in the company. Also, apart from the missing direct connection between the two, it seems that the controlling variables are not of use either.
However, this research does add to existing findings regarding the linkage between job security and job satisfaction. Proven already by others in many other industries, a high feeling of job security can often lead to a high feeling of job satisfaction. This research shows that this is once again applicable, but now from the perspective of the grocery store industry.
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IX. Appendix
1. The survey
Welcome to the survey made for my bachelor research thesis. Please take your time to read this introduction. The estimated time of completion is approximately 4 minutes. Some important points concerning the survey:
- Always read the question description on the top of the page
- This data collection is anonymous and confidential. I don't know who answers what, and I don't have any personal information about you.
- Your contribution is voluntary. If you do decide to participate, you will have the option to leave your email address at the end of the survey. If you do, you will receive a short summary report of the findings.
Thank you very much for your participation, Luc de Vries
Bachelor student
University of Amsterdam Amsterdam Business School
Q8 How do you identify? m Man (1)
m Woman (2)
Q10 What is your age category? m <18 (1) m 18-25 (2) m 26-30 (3) m 31-40 (4) m 41-50 (5) m 51-60 (6) m >60 (7)
Q17 What is the highest educational level you have finished? m University (1) m HBO (2) m MBO (3) m High school (4) m Other (5)
Q16 What is your function? m Manager (1) m Team leader (2) m Cash-register employee (3) m Re-stocker of shelves (4) m Self-Scan employee (5) m Different... (6)
Q9 Which type of employment applies to you? m Fulltime (1)
m Part-time 60%-99% (2) m Part-time (3)
Q11 Does the Albert Heijn where you are employed has the Self-Scanner system implemented? m Yes (1)
m No (2)
Q10 At what kind of Albert Heijn are you employed?https://www.ah.nl/over-ah/winkels m Regular Albert Heijn (wijkwinkel) (1)
m Albert Heijn to go (2) m Albert Heijn XL (3)
Q13 Some Albert Heijn stores are more often being used to test out new concepts or ideas than others. How would you rate the amount of innovative concepts or ideas that are being tested als a pilot in your store?
m Never (1) m Sometimes (2) m Often (3)
Q18 How do you feel about technology innovations in general? m very positive (1) m positive (2) m neutral (3) m negative (4) m very negative (5)
Disagree very much (1) Disagree moderately (2) Disagree slightly (3) Agree slightly (4) Agree moderately (5) Agree very much (6)
I feel I am being paid a fair
amount for the work I do. (42) m m m m m m
There is really too little chance
for promotion on my job. (43) m m m m m m
When I do a good job, I receive the recognition for it that I should receive. (46)
m m m m m m
Many of our rules and procedures make doing a good job difficult. (47)
m m m m m m
I like the people I work with.
(48) m m m m m m
I sometimes feel my job is
meaningless. (49) m m m m m m
Those who do well on the job stand a fair chance of being promoted. (52)
m m m m m m
My supervisor is unfair to me.
(53) m m m m m m
The benefits we receive are as
good as most other
organizations offer. (54)
m m m m m m
I do not feel that the work I do
is appreciated. (55) m m m m m m
I find I have to work harder at my job because of the incompetence of people I work with. (57)
m m m m m m
I like doing the things I do at
work. (58) m m m m m m
My supervisor shows too little interest in the feelings of subordinates. (62)
m m m m m m
I have too much to do at work.
(65) m m m m m m
I often feel that I do not know what is going on with the organization. (67)
m m m m m m
I feel a sense of pride in doing
Very unlikely
(1) Unlikely (2)
Neither likely nor unlikely
(3)
Likely (4) Very likely (5)
Lose your job and be moved to a lower level job within the organization? (Q13_1)
m m m m m
Lose your job and be moved to another job at the same level within the organization? (Q13_2)
m m m m m
Find that the
number of hours the company can offer you to work may fluctuate from day to day? (Q13_3) m m m m m Be moved to a higher position within your current location? (Q13_4) m m m m m
Q13 Thinking about the future, how likely is it that each of these events might actually occur to you in your current job?
Be moved to a higher position in another geographic location? (Q13_5) m m m m m Find your department or division's future uncertain? (Q13_6) m m m m m
Lose your job by being fired? (Q13_7)
m m m m m
Lose your job
by being pressured to accept early retirement? (Q13_8) m m m m m
Q9 Thank you very much for your participation! If you would like to receive a brief report about the findings of my research, please leave your email address here.
