Impact of End-User Computing on the Individual: An ...

Information Technology & People, 6:4 (1994) 271-292

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Impact of end-user computing on the individual: An integrated model Magid Igbaria Department of Management College of Business & Administration Drexel University Philadelphia, PA 19104 Kranti Toraskar Department of Information Systems City Polytechnic of Hong Kong Kowloon Tong, HONG KONG Fax: 852-788-8694

ABSTRACT This study formulates and tests an integrated model of the determinants of end-user computing (EUC) success, and their direct effects on individual users' job satisfaction as well as quality of work-life. Hierarchical multiple regression analysis was employed to test the model using data generated through a questionnaire survey of 177 individuals working in EUC environments. The results demonstrate that the impact of end-user computing at the individual level is co-produced by EUC success as well as by the end-user attitudes toward EUC. While the user attitudes showed a direct impact on EUC success, the latter was also affected by the organizational support for EUC. Finally, end-user attitudes themselves were found to be influenced by the individual's computer experience, training, and organizational level. Overall, the study extends existing EUC research by focussing specifically on the determinants of EUC success and their impact on jobs and work.

INTRODUCTION As the microcomputer and telecommunication technologies intensify the rapid growth of end-user computing (EUC) in organizations, currently researchers and practitioners are compelled to investigate the impact of EUC on affected jobs, and its consequences for individual end-users. Many recent studies in this area (Attewell & 0959-3845

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Rule, 1984; Bikson, et al., 1985; Cheney & Dickson, 1982; Coates, 1988; Kaye & Sutton, 1985; Kraut, et al., 1989; Millman & Hartwick, 1987; Morell, 1988; Porter, 1987; Turner, 1984; Weber, 1988) refer to the impact of new information technologies on end-users in terms of job satisfaction and quality of work-life (QWL). Although these studies have been useful in identifying the broad nature, and domains, of EUC impact on individuals using computer-based information systems, they contain many contra­ dictory and inconclusive reports about the EUC impact. Consequently, additional research is needed to address as yet unanswered questions. As a pervasive phenomenon involving the use of information technology in the business world today, EUC has become a major topic within the field of MIS, and has generated considerable interest among MIS practitioners (Guimaraes, 1984; Johnson, 1984) as well as researchers (Alavi & Weiss, 1986; Doll & Torkzadeh, 1988; EinDor & Segev, 1988; Panko, 1987; Rivard & Huff, 1988; Rockart & Flannery, 1983). However, most academic research to date in this area has been aimed primarily at the identification and definition of basic concepts, problems, and issues related to EUC (Benson, 1983; Brancheau, et al., 1985; Moore, 1987). Such issues include: the management of EUC (Rockart & Flannery, 1983), user development of computer based applications (Rivard & Huff, 1984), end-user satisfaction (Amoroso, 1988; Doll & Torkzadeh, 1988; Rivard & Huff, 1988), extent of use (Ein-Dor & Segev, 1988), and the use of EUC in small businesses (Raymond, 1987). In contrast, relatively little is known by way of research regarding the impact of EUC on the work involved, specifically the office jobs, and on end-users as individuals. Therefore, the first objec­ tive of this study is to examine relationships between EUC success and its impact on the individual end-user. Prior studies in this area (Kraut, et al., 1989; Turner, 1984) have generally examined only bivariate relationships of system usage with the im­ pacts of new technology, but have ignored the possible multivariate linkages in­ volving a variety of determinants of EUC success and their effects on the overall work-life of individuals. The present study aims to investigate explicitly such multi­ variate linkages in the context of EUC. Although speculations about the impact of computers on individuals' social and work experience date back nearly two decades (Martin, 1971), a systematic body of research in this area has started accumulating only recently. In particular, computer technology seems to affect the nature of office-work, job satisfaction of the officeworkers, and their quality of social and working life, according to several recent studies. Turner (1984) reported that office-worker-interactions with clients, and the office-workers' perceptions of their task environment and work-related wellbeing, were all affected by the type of system interface used. Coates (1988), Kaye and Sutton (1987), and Kraut, et al. (1989) found that computerization had affected office-work productivity, as well as the quality of work-life (QWL) of those involved. These studies found that the work-life of individuals who use computerized systems generally deteriorated in quality, had less variety, was less challenging, and the individuals were less able to see the results of their work. Such research findings corroborate Olson and Lucas's (1982) propositions concerning the negative impact of

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office automation in terms of work related stress, perceived status and job satisfac­ tion, particularly among the lower echelons of organizational hierarchy. On the other hand, Millman and Hartwick (1987) found that middle managers believe office automation gave them increased work-autonomy, discretion and control over the results of their work. Similarly, Bikson, et al. (1985) reported that the major­ ity of users felt their work had been enriched by computers. These contradictions regarding the impact of new technology on job satisfaction have been emphasized by Attewell and Rule (1984). While Bikson, et al. (1985), Cheney and Dickson (1982), and Porter (1987) have reported that computer-based information systems increased the level of overall job satisfaction, others have found that workers using such sys­ tems show significantly lower level of job satisfaction (Kraut, et al., 1989; Turner, 1984). In response to such conflicting and inconclusive findings of the existing research, this study aims to clarify some of the distinctions in EUC-success and its impact on individuals, specifically as reflected in job satisfaction and quality of work-life. A number of constructs have been used to describe MIS success in the recent literature. Among these are end-user satisfaction (Bailey & Pearson, 1983; Baroudi, Olson & Ives, 1986; Baroudi & Orlikowski, 1988; Cheney & Dickson, 1982; Doll & Torkzadeh, 1988; Ives, et al., 1983; Montazemi, 1988; Raymond, 1985; Rivard & Huff, 1988), decision quality and performance (King & Rodriguez, 1978; Lucas, 1975), user development of applications (Rivard & Huff, 1984), and the level of system usage (Baroudi, et al., 1986; Ein-Dor & Segev, 1978; Igbaria, et al., 1989; Jobber & Watts, 1986; Lucas, 1978). The most generally accepted measures of system success appear to be the end-user "satisfaction" and level of system usage (Ein-Dor & Segev, 1982; Ginzberg, 1978). These measures have been selected as the appropriate indicators of system success in this study. One problem with the majority of the previous studies of system success is that they have been conducted primarily in the context of traditional mainframe environ­ ments (Rousseau, 1989; Cheney & Dickson, 1982; Turner, 1984; Kraut, et al., 1989). In these studies, most of the attention is focussed on a vague notion of end-user satisfaction, as if it were the sole indicator of system's success. As a result, in the midst of the rapid growth of end-user computing (Benson, 1983; Cheney, et al., 1986; Panko, 1987), the real determinants of system success in the EUC environment might remain largely unexplored. More research is needed to examine the configuration of variables spe­ cifically related to EUC success, as measured by system utilization and end-user satis­ faction. Accordingly, the principal goal of this study is to formulate and test an integrated model of the determinants of EUC success, and their direct effects on the individual end-user's job satisfaction and the quality of work-life (QWL). The model involves multivariate relationships of major individual and organizational variables with EUC success, as specifically operationalized through system utilization variables and the notion of end-user satisfaction. In addition to testing the comprehensive character of the proposed model, the analysis strategy of this study is aimed at two related

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sub-goals: (1) to investigate the relationships of major individual and organizational characteristics to EUC success, and (2) to examine the impact of EUC success on the end-user as a working individual.

THE PROPOSED MODEL AND STUDY HYPOTHESES Figure 1 shows all of the variables involved in our proposed model of the impact of EUC on individuals. The integrated model includes the following three sets of variables: (a) Individual and Organizational variables: demographics, end-user training, computer experience, attitudes toward EUC (characterizing the individual), and information center (IC) support (characterizing the organization); (b) EUC Success variables: end-user satisfaction and system utilization (measured by the number of software applications, number of tasks, actual daily use of the system, and frequency of use of the system); and finally, (c) Individual Impact variables: job satisfaction, communication and social interaction changes. The model is developed based on the theoretical perspectives provided by Attewell and Rule (1984) and Zmud (1979), and the conceptualizations of MIS success in general (Cheney, et al., 1986; Igbaria, et al., 1989; Lucas, 1978). The importance of these variables and the rationale for their inclu­ sion in our model is discussed below with reference to the existing literature. The numbered arrows in Figure 1 identify the major hypothesized relationships of interest in this study. In broad terms, the importance of major individual as well as organizational level variables has been emphasized in relation to MIS success in general by Mason and Mitroff (1979), Lucas (1978), Zmud (1979) and others. The variables in this category include factual demographic descriptors of the individual (e.g., age, gender, and organizational level) as well as perceptual variables (e.g., end-user satisfaction and attitudes toward the system). Additionally, in the EUC environment organizational

Impact of end-user computing on the individual

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support is considered a very important factor affecting EUC success (Rockart & Flannery, 1983). Since the information center (IC) concept represents the most impor­ tant form of organizational support in the EUC environment, IC support is included in this study to reflect the organizational support to end-users. Based on relevant literature, possible relationships between EUC success and the specific model variables are presented below in the form of the study hypotheses. The possible nature of the impact of EUC on end-users' job satisfaction, and on their quality of work-life (QWL), is also discussed. Hypothesis 1 proposes relationships between individual characteristics and attitudes toward EUC. The proposed relationships are based on literature about effects of individual characteristics (as well as organizational characteristics) on the "attitudes" toward computerized information systems. Attitudes, in general, are defined as an individual's perceptions of an object on a like-dislike type of continuum (Fishbein, 1967), and can be measured using Likert-type response format. The significance of attitudes in relation to organizational information systems derives from the proposi­ tion of attitude theorists (Fishbein, 1967; Fishbein & Ajzen, 1975) suggesting that individuals' attitudes toward an object play an important role in influencing their subsequent behavior toward it. The demographic/situational variables included in this model are gender, age and organizational level of the individual. These variables have been found to be related to attitudes toward a computer-based information systems in recent studies. Gender has been found to be related to attitudes toward computerized systems in some studies (Collis, 1985; Dambrot, et al., 1985), while other studies show men and women to be similar in their attitudes toward systems (Howard & Smith, 1986; Igbaria & Parasuraman, 1989; Parasuraman & Igbaria, 1990). More general social psychologi­ cal research (Kessler, et al., 1983; Naiman, 1982) suggests that computers and math­ ematics are perceived as belonging primarily to the male domain. In view of these major differences of perspective in the research, we find it necessary to investigate the role of gender in the success and impacts of EUC. Age has been reported to be negatively related to computer attitudes (Igbaria & Parasuraman, 1989; Raub, 1981). Age is associated with less computer knowledge and training, less flexibility, and consequently with less favorable attitudes toward computerized information systems in general. Zmud (1979) identifies organizational level as a factor potentially related to user attitudes. It has been reported that, as compared to their staffs, middle managers and executives are intimidated by computer jargon, and report fewer hours of system usage per week (Igbaria, et al., 1989; Lee, 1986; Margarita, 1985). Therefore, organizational level may be associated with more negative attitudes toward the system. Furthermore, computer experience and enduser training have been found to be positively related to attitudes toward the system (Igbaria & Chakrabarti, 1990; Galagan, 1983; Howard & Smith, 1986), and positively related to system usage (DeLone, 1988; Fuerst & Cheney, 1981; Igbaria, et al. 1989; Lee, 1986; Schewe, 1976). In summary, based on existing research, the attitudes toward

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EUC can be expected to be negatively related to both age and organizational level, while being positively related to computer experience and end-user training. Finally, there is some indication that women will report less favorable attitudes toward EUC than men. Several researchers have emphasized the importance of organizational support as a potential determinant of system success (Amoroso, 1988; Ein-Dor & Segev, 1978; Fuerst & Cheney, 1982; Lucas, 1978; Rivard & Huff, 1988; Robey & Zeller, 1978). Organizational support has been found to be positively associated with favorable attitudes toward systems (Igbaria & Chakrabarti, 1990), while lack of organizational support is considered a critical barrier to effective utilization of computers (Fuerst & Cheney, 1982; Lee, 1986; Lucas, 1978). In the present context, organizational support for EUC refers to those activities which serve to enhance the growth of EUC within the organization. Examples of EUC support activities include user-training programs that can identify and address the informational needs of end-users and others. The Information Center (IC) concept has been recently recognized as the most common type of delivery mechanism for EUC support activities (Bergeron & Berube, 1988; Carr, 1987; Magal, et al., 1988). Infor­ mation centers are intended to facilitate access to, and usage of, a variety of sys­ tems and services available to end-users. The IC concept provides management with a coordination of usage and accountability for the various information sys­ tems resources involved. Organizational support for EUC through an IC is con­ sidered essential for implementing training programs that enable end-users to use software tools effectively, and foster continued user-learning in general. The contribution of IC-based training of end-users toward EUC success has been ac­ knowledged by researchers as well as practitioners of MIS (Igbaria & Chakrabarti, 1990; Nelson & Cheney, 1987; Sein, et al., 1987). We hypothesize that organiza­ tional support is positively related to attitudes, usage, and satisfaction. Hypothesis 2, indicated by the numbered arrow in Figure 1, proposes relation­ ships between EUC success and the end-user attitudes toward EUC. Following the attitude theory (Fishbein, 1965; Fishbein & Ajzen, 1975) proposition of the impor­ tance of attitudes in shaping individual behavior, a number of information systems researchers have found that user attitudes toward computerized information systems are strongly related to their success (Ein-Dor & Segev, 1978; Igbaria, et al., 1989; Lucas, 1978; Maish, 1979; Rivard & Huff, 1988; Robey, 1979; Tait & Vessey, 1988). The general prediction is that favorable attitudes toward EUC are associated with the higher levels of EUC success. Hypothesis 3 is concerned with the relationship of EUC success to the individual's job satisfaction and quality of work-life (QWL). In contrast to the specific concept of job satisfaction, QWL refers to a much broader set of variables characterizing an individual's work-life. They include perceived variety, challenge, and impact of one's own work, as well as work-related autonomy, status, stress, and social interactions (Bikson, et al., 1985; Kraut, et al. 1989; Turner, 1984). Here, the general prediction is

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that end-users who use a system more often would report greater satisfaction with the system. This system-related satisfaction, in turn, is related to greater job satisfaction and an enriched quality of work-life. Investigation of this hypothesis is potentially valuable, since as discussed below the literature shows considerable controversy and conflicting findings in this area as well. Since the focus of this study is on job satisfaction and QWL, rather than on user's satisfaction with the system, based on the work of Kraut, et al. (1989), here we are interested the path from system usage to job satisfaction. The concerns over possible negative effects of computers at the macro social and organizational levels (Whisler, 1970), as well as at the individual level (Martin, 1970), are not new in the MIS literature. However, over the years, little consensus has emerged regarding such effects, owing to a wide variety of mutually incompatible research perspectives used (Rice, 1986). More recent social and MIS research studies also seem to suggest that, while information technology may increase productivity, it can degrade the work and the social life of those who use it (Olson & Lucas, 1982). Contradictory findings have been reported about the precise nature and direction of these effects of information technology on workers and their jobs (Attewell & Rule, 1984; Bikson, et al., 1985; Caporael, 1984; Cheney & Dickson, 1982; Coates, 1988; Counte, et al., 1985; Kaye & Sutton, 1985; Kraut, et al., 1989; Liker, et al., 1987; Millman & Hartwick, 1987; Olson & Lucas, 1982; Porter, 1987; Turner, 1984; Weber, 1988). While one set of researchers reports a significantly increased level of job satisfaction attributed to computerized information systems (Cheney & Dickson, 1988; Counte, et al., 1985, Millman & Hartwick, 1987), others find a decreased level of overall job satisfaction due to automation (Kraut, et al., 1989; Turner, 1984). Zuboff (1988) has argued that computerization has added intellectual content to work, whereas Kraut, et al. (1989) have found that the quality of work-life of individuals who use the system became poorer and had less variety. On the other hand, Bikson, et al. (1985), Mittman and Hartwick (1987), and Porter (1987) reported that in presence of office automation, middle managers found their jobs more enriching, and the quality of work-life was enhanced. Finally, Kaye and Sutton (1985) claim that "the overall im­ pact seems to have been positive for the majority of participants", even as they ac­ knowledge the possibility of both positive and negative impacts. The confusion of such contradictory findings regarding the effects of computing on organizations is explicated by Attewell and Rule (1984). They argue that research evidence in this area is "fragmentary and very mixed" (p. 1184) and "none of the studies mounted so far have been capable of yielding a persuasive and comprehen­ sive view of computer-induced social change" (p. 1185). In particular, they point to "the (wide) range and variety of variables at work in these situations" (p. 1184) as one of the challenges involved. Accordingly, this study seeks to extend previous research by investigating, through a single model, the variety of multivariate rela­ tionships among the individual and organizational variables, the system usage and user satisfaction, as well as the impact on individuals' job satisfaction and QWL. As shown in this section, the model is based on the available research to the extent

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possible. The design of the study for examining the network of various relationships is elaborated next, in terms of the study sample, operational measures, and the data analysis techniques used.

METHOD Sample A questionnaire, presented as a survey of end-user computing, was distributed to part-time MBA students who were full time employees holding professional or managerial positions in a variety of manufacturing, service, retail, and government organizations in the northeast of the United States. Participation in the survey was voluntary, and subjects were assured that their responses would be treated as confidential. Out of the 230 questionnaires distributed, 189 were completed and re­ turned representing a response rate of 82 percent. The exclusion of responses from part-time employees and incomplete questionnaires resulted in a final sample of 177 respondents. Of the 177 usable questionnaires, 126 (71 percent) were received from men, and 51(29 percent) from women. The respondents ranged in age from 23 to 42, with a median of 27 years. Fifty-seven percent of the respondents held professional positions at their employment, while the remaining (43 percent) held managerial positions in a wide range of functional areas including accounting, finance, market­ ing, sales, production, and general management. Table 1 presents the demographic profile of the sample. Table 1. Profile of Respondents Gender:

Male = 7 1 %

Female = 29%

Age:

Mean = 28.5

Median = 27.0

Organizational Level: Professionals First level supervisor Middle management Strategic management

Range = 23-12

57% 26% 15%

2%

OPERATIONAL MEASURES OF THE STUDY VARIABLES Impacts on the Individual's Job and Quality of Work-life: The impacts of EUC on the individuals were assessed through their job satisfaction and quality of work-life (Bikson,etal.,1985; Cheney & Dickson, 1982; Kraut, et al., 1989; Morell, 1988). Job satisfaction

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was measured with items adapted from (Bikson, et al., 1985; Kraut, et al., 1989). Individuals were asked to indicate what has been the general impact of EUC work on their job satisfaction, and kind of projects they work on in their job. Each item was assessed on a three point scale: (1) less; (2) same; and (3) more. The two items were averaged to create a job satisfaction score. The quality of work-life or QWL was measured by a four item scale based on Bikson, et al. (1985) and Kraut, et al. (1989). Each item required the respondents to indicate the direction of change they perceived, due to EUC, in specific work-life variables, such as, the variety/variability of work, control over their work (autonomy), the challenge, and the impact (e.g., observable results) of their work. Adopted from the previous research (Bikson, et al., 1985; Kraut, et al., 1989), the response options are (1) less, (2) same, and (3) more. The four items were summed and averaged to obtain an overall index of the quality of work-life. The homogeneity, or similarity, of items within each measure was established by computing their internal consistency reliability coefficient, Cronbach's alpha (.81 for the job satisfaction measure, and .78 for the QWL measure). End-User Satisfaction: End-user satisfaction was measured by a twelve item scale developed by Doll and Torkzadeh (1988). The scale is a measure of overall EUC satisfaction as well as satisfaction with the extent to which the computer application meets the end-user's needs with regard to information content, accuracy, timeliness, format and ease of use. Each item was measured on a five point Likert scale ranging from (1) "almost never" to (5) "almost always". Doll and Torkzadeh (1988) reported the results of a factor analysis that produced five interpretable factors: Content, accuracy, format, ease of use, and timeliness. In our study, a factor analysis (with varimax rotation) produced two factors, one tapping the "output" component of end-user satisfaction (e.g., content, accuracy and timeliness), and the other assessing the "quality" component of end-user satisfaction (e.g., format, ease of use). However, since a second-order factor analysis revealed that one global end-user satisfaction factor explained 86.1 percent of the variance, the entire set of 12 items was averaged to produce an overall end-user satisfaction scale. The internal consistency reliability of the whole scale in this study was .93. System Utilization: Based on previous research on MIS usage (Cheney, et al., 1986; DeLone, 1988; Igbaria et al., 1989; Lee, 1986; Raymond, 1985; Srinivasan, 1985) this study included four indicators of EUC utilization as described below. Number of Tasks (involving the use of computer). This indicator adopted from Cheney, et al. (1986), and Igbaria, et al. (1989), measures the extent to which respondents included computer analysis in performing their job-related tasks. It was developed based on 13 tasks for which the system could be used, for example, looking for trends, planning, budgeting, communicating with others. The participants were asked to indicate whether they personally used the EUC system to perform these tasks. A yes/no response format was used, and the sum of the 13 items was used as an overall indicator of this measure.

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Number of Applications Used: The integration between microcomputers and host computers in the EUC environment brings more variety and diversity to the software applications available to end-users (Igbaria, et al., 1989; Lee 1986; Panko, 1987). Observation of the diversity of such applications, utilized by the end-users, can provide a good indication of the overall system utilization in the EUC environment. The questionnaire used in this study listed ten different types of software applica­ tions, such as, word processing, spreadsheets, database management, graphics, electronic mail (Igbaria, et al., 1989; Panko, 1987). The participants were asked to indicate whether they personally used these applications. The sum of the ten appli­ cations was used as an overall indicator of this measure. The total daily Time of Use, and the Frequency of Use, of the EUC system were adapted from Igbaria, et al. (1989), Lee (1986), and from Mittman & Moore (1984). End-users were asked to indicate the amount of time spent on the system per day, using a six point scale ranging from (1) "almost never" to (6) "more than three hours per day". Frequency of use was measured on a six-point scale ranging from (1) "less than once a month" to (6) "several times a day". Attitudes Toward EUC: This measure assesses an individual end-user's attitudes toward personally using an EUC system in accomplishing job related tasks. The ten items used to construct this attitudinal measure were adapted from prior research (Cheney, et al., 1987; Goodhue, 1988; Igbaria & Parasuraman, 1989; Igbaria, et al., 1989; Rivard & Huff, 1988) with appropriate modifications to make them relevant to the EUC environment. End-users were asked to indicate the extent of agreement or disagreement with a list of ten statements about EUC. Sample statements included are: "Using an EUC system could provide me with information that would lead to better decisions"; "Using an EUC system can take u p too much time in perform­ ing my tasks"; "Using an EUC system improves my productivity on the job". A Likert-type response format was provided, with response options ranging from (1) "disagree strongly" to (5) "agree strongly". The ten items were summed and averaged to obtain an overall index of attitudes toward EUC. The internal consis­ tency reliability of the ten item scale was .82. EUC Support: The measure of EUC support refers to EUC related support activities which include training and education, consulting in the areas of problem solving and application development, and data access for EUC applications (Bergeron & Berube, 1988; Carr, 1987; Magal, et al., 1988; Nelson, 1989). The scale consisted of four items representing EUC support provided by IC. End-users were asked to indicate the extent of their agreement or disagreement with each of the four items on a five point Likert type scale ranging from (1) "strongly disagree" to (5) "strongly agree". The four items were averaged to obtain a measure of EUC support (alpha = .73). Computer Experience: Computer experience was assessed by asking end-users to indicate whether they had experience in using different types of computer software, different computer languages, and development of computerized information systems. Responses were coded "0" (zero) for no experience and " 1 " for some experience. The

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total number of categories in which end-users reported experience was used as an overall index of computer experience. End-user Training: End-users were asked to indicate the extent of training they had received from four resources: vendor, college, company, a n d / o r self training (Benson, 1983; Nelson & Cheney, 1987). The response options ranged from (1) "none" to (5) "very extensive". The mean of the responses to these four questions was used as an indicator of end-user training. Single item questions were used to ascertain respondents' gender, age, and orga­ nizational level. Since all the participants were part-time MBA students, education was not considered. Age ranged from 23 to 42, and the mean age of the respondents was 28.5 years. Organizational level consisted of four tiers, ranging from (1) "profes­ sional" to "strategic management (executives)".

Data Analysis The technique of path analysis using least squares multiple regression was used to determine whether the observed pattern of relationships among the variables was consistent with the causal model presented in Figure 1. The choice of path analysis over structural equation modeling techniques such as LISREL is based on the small sample size and the well-established measures for all the variables (for more details, see Fornell, 1982). However, it was necessary to first take several steps to check for possible violations of the assumptions underlying the use of path analysis (Billings & Wroten, 1978; Heise, 1969). An examination of the alpha coefficients indi­ cated satisfactory levels of internal consistency reliability among all the multi-item scales (alpha coefficients ranged from .73 to .93). The intercorrelations ranged from -.23 to .66 and the median intercorrelation was -.03, failing to reveal evidence of extreme multicolinearity (i.e., r's≥.80). Additionally, the residuals of the endogenous variables were tested for autocorrelation using the Durbin-Watson d-statistics (Dillon & Goldstein, 1984). This analysis revealed a distribution of the d-statistics (mean = 2.00, range = 1.88 to 2.18) that is strongly indicative of the absence of correlated residuals. The application of path analysis to theory development often involves two sets of path analyses to examine the pattern of relationships among variables in a model (Heise, 1969). Initially, a fully recursive model was examined in which multiple regression analyses were performed to assess all possible direct effects of the ante­ cedent variables on each dependent variable. In order to provide a more parsimoni­ ous representation of the data, the model was trimmed by deleting the nonsignificant paths revealed by the initial analyses, and by excluding those variables (from a given analysis) that failed to show any direct or indirect relationship with the dependent variable being predicted. The omitted parameter test (James, et al., 1982) was used to determine whether the paths included in the path model were statistically significant, and whether or not

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282

the unpredicted paths were significant. This test involves testing all the direct paths (predicted or unpredicted) among the model variables. In this manner, one identifies all possible ways to confirm or reject the proposed model. Hierarchical multiple regression (Cohen & Cohen, 1983) was performed to conduct the omitted parameter test, and to assess the direct and indirect effects of the indepen­ dent variables on each dependent variable. In order to test Hypothesis 1, the attitudinal variable (Attitudes toward EUC) was regressed on demographic factors (i.e., age, gen­ der and organizational level) in the first step, with end-user factors (i.e., computer experience and end-user training) and organizational support added to the equation in the second step of the analysis. In order to test Hypothesis 2, the EUC success indica­ tors (end-user satisfaction and each system utilization variable) were each regressed on demographic factors in step 1, with end-user factors and organizational factors (EUC support) added in step 2, and attitudes toward EUC added in step 3. Hypothesis 3 was tested by regressing the impacts on individuals (i.e., job satisfaction and quality of work-life) on demographic factors in step 1, with end-user factors and organizational factors (EUC support) added in step 2, attitudes toward EUC added in step 3, and EUC success added in step 4. In each analysis, the significance of the beta weights for the hypothesized independent variable was examined to determine support for the hy­ pothesis. In addition, Cohen and Cohen (1983) suggested that the increment in R2 (∆R2) should be used to determine the relative importance of contribution of each of the predictor variables to variation in attitudes, success and the impact on individuals. Table 2. Matrix of Intercorrelations among Study Variables (n=177) Variables

1

Gender [1=M, 2=F]

Mean 1 29

SD

1

2

3

4

5

6

7

8

9

10

11

12

13

14

45 1 00 6 75 - 16 1 00

2. Age

28 5

3. Organizational Level

1 66

4

End-User Training

2 81

5

Computer Experience

5 64

6

EUC Support

3 39

95

08 - 02

02

25

21 1 00

7. Attitudes Toward EUC

4 07

62

07 - 03 - 22

30

25

18 1 00

8. End-User Satisfaction

3 68

76

04 - 13 - 08

31

21

40

30 1 00

87 - 15

28 1 00

84 - 06 - 07 - 13 1 00 1 34 - 09 - 01 - 01

49 1 00

9. Number of Tasks

8 58

4 42 - 02

08 - 05

36

37

19

28

16 1 00

10 Number of Applications

4 65

2 28 - 11 - 04 - 19

47

46

13

36

10

31 1 00

11 Time of Use

4 42

1 48

05 - 03 - 25

42

32

16

45

30

37

23 1 00

12 Frequency of Use

5 07

1 24 - 05

45

40

20

46

32

46

36

66 1 00

13 Job Satisfaction

2 62

48 - 04

14 - 10

15

16

04

32

07

16

01

24

22 1 00

14 Quality of Work Life

2 63

34

10

02 - 17

20

18

28

38

22

35

16

21

29

02 - 06

Note: The absolute value of correlations ≥ .12 are significant at .05 level or better.

44 1 00

Impact of end-user computing on the individual

283

RESULTS The intercorrelations among the study variables are shown in Table 2. The results revealed significant positive correlations between attitudes toward EUC and enduser training, computer experience, and organizational support, and a negative relationship between attitudes toward EUC and organizational level. Furthermore, all of the EUC-success indicators (namely, end-user satisfaction, frequency of use, time of use, number of applications, and number of tasks) were found to be positively and significantly correlated with attitudes toward EUC, user training and computer experience. EUC support was found to be positively correlated with all of the EUCsuccess indicators, and with the quality of work-life (QWL). In addition, Table 2 shows that job satisfaction is positively and significantly correlated with frequency of use, time of use and with number of tasks, and that quality of work-life is correlated significantly and positively with each of the EUC-success indicators. The results of the hierarchical multiple regression analyses predicting attitudes toward EUC are presented in Table 3. Consistent with Hypothesis 1, top management reported less favorable attitudes toward EUC, and computer experience and enduser training were found to have positive effects on attitudes toward EUC. However, women and men showed no differences in their attitudes toward EUC. Similar results were found with older people, with no significant effects of age and support on attitudes toward EUC. Table 3. Results of Hierarchical Regression Analyses Predicting Attitudes toward EUC and EUC Success Indicators (n=177) Attitudes Toward EUC Independent Variables Beta R2

End-User Satisfaction

∆R2 Beta R2

Num of Tasks

∆R2 Beta R2

Num of Applications

Time of Use

AR2 Beta R2

AR2 Beta R2

∆R2

Frequency of Use Beta R2

AR2

Demographics Gendera

01

-01

-03

-14

-02

Age

02

-11

04

-03

01

Organizational Level

-22 * 04

04

- 06

02

02

-04

01

01

-09 -03

-20 * 06 * 06 * -26**06 *

06 * - 04

01

01

End-User Factors End-User Training

21 *

19 *

34 ***

27 **

37 ***

34 ***

Computer Experience

21 *

18 *

25 **

35 ***

22 **

30 ***

Organizational

Factors

EUC Support

08

AttitudesTowardEUC a *

1 = Male, 2 = Female p ≤ 05

**

p ≤ 01

*** p ≤ 001

19 *** 15 ** 27**19**17 *** 03

24 *** 23 *** -02

36***30***11 *** 22**29 ***05 **

33***27 *** 04

32 *** 26 *** 03

30***29 ***

24**33***05 ** 32***40 ***08 *** 34***39***09 ***

Impact of end-user computing on the individual

284

Results of the analyses for predicting the five indicators of EUC success are also presented in Table 3. Consistent with Hypothesis 2, attitudes toward EUC revealed a positive and significant relationship with each of the EUC success indicators. Also, the attitudinal predictor variable explained a significant portion of the variance in end-user satisfaction (11 percent), with the higher levels of satisfaction associated with favorable attitudes. Table 3 also shows that step 2 of the analysis for Hypothesis 2 (involving enduser training, computer experience and EUC support) explained 17, 23, 27, 26, and 29 percent of the variance in end-user satisfaction, number of tasks, number of applica­ tions, time of use, and frequency of use, respectively. It should be noted that both user training and computer experience had effects on each of EUC success indicators. In contrast, EUC support is positively related only with end-user satisfaction. Table 4 presents the results of the analyses predicting job satisfaction and quality of work-life (QWL). Of all the EUC success indicators, only the "frequency of use" was found to be significantly related with job satisfaction, and only "number of tasks" to be positively and significantly correlated with quality of work-life. However, the EUC success indicators did explain a significant portion of the variance in job satisfaction (9 percent, p ≤ .05). Moreover, attitudes toward EUC had positive and significant effects on both job satisfaction and quality of work-life, whereas EUC support was found to be positively and significantly related only to quality of work-life. Attitudes toward EUC explained 9 and 7 percent of the variance in job satisfaction and quality of work-life, respectively. Finally, EUC support explained 8 percent of the variation in QWL. Table 4. Results of Hierarchical Regression Analyses Predicting Job Satisfaction and Quality of Work Life (n=177) Job Satisfaction Independent Variables Demographics Gendera Age Organizational Level End-User Factors End-User Training Computer Experience Organizational Factors EUC Support Attitudes Toward EUC EUC Success Indicators End-User Satsifaction System Utilization Number of Tasks Number of Applications Time of Use Frequency of Use a

1 = Male, 2 = Female * p ≤ 05 ** p ≤ 01 *** p ≤ 001

Beta

R2

-08 15 -04

03

Quality of Work Life AR2

03

12 -08 03 33

R2

05 01 -12

AR2

02

02

12 09

***

04 .13 *

01 09 ***

04

21* 28

**

.10 * 17

**

23

**

08 * 07 **

-01

-08 -11 23 *

Beta

22 * -08 22 **

21 09 *

21

-19 06

Impact of end-user computing on the individual

285

DISCUSSION The present study examined the factors which contribute to EUC success, and to the resultant impact of EUC on the end-user as an employee of the organization. The analysis results presented above generally provide a moderate support for the pro­ posed model. In particular, the results demonstrate the importance of examining the individual-level end-user factors when explaining the variations in attitudes toward EUC. Furthermore, the attitudes toward EUC, the organizational support for EUC, as well as the end-user factors, were found to have significant effects on most of EUC success indicators (namely, end-user satisfaction, number of tasks, number of appli­ cations, time of use and frequency of use). The findings also indicate the relevance of end-user attitudes, and that of EUC success, on the job satisfaction and QWL of the individuals working in EUC environments. Out of the demographic variables examined, only the organizational level affected attitudes toward EUC and this effect was negative. Individual characteristics or enduser factors like computer experience, and end-user training, were found to have positive effects on attitudes toward EUC. The results also indicate that men and women are similar in their attitudes toward EUC and unrelated with all the EUC success indicators. This suggests that observed gender differences reported in previous research may partially be a function of the lower status organizational positions and roles occupied by women relative to men (Gutek & Bikson, 1985; Howard & Smith, 1986; Parasuraman & Igbaria, 1990). In view of the increasingly knowledge-work oriented organizations of today, the above findings have significant management implications, specifically for the human resource management (HRM) function. In broad terms, for knowledge intensive organizations, it may be a better HRM strategy to focus on developing the end-user factors (e.g., computer experience) through training, than on the demographics during recruiting. Consistent with our expectations, end-user train­ ing is associated with favorable EUC attitudes, and also promotes increased EUC success. A powerful practical implication of this result is that, in this EUC era of organizational information systems, end-user training programs may in fact be a survival need, rather than a discretionary expense, for most organizations. The negative effects of organizational level on attitudes, and other EUC success indi­ cators, also emphasize the need for specially designed training programs for top management to increase their familiarity with the advantages of EUC (Amoroso, 1988; Carr, 1985; Cheney, et al., 1987; Igbaria & Chakrabarti, 1990; Igbaria, et al., 1989; Nelson, 1985; Rockart & Flannery, 1983; Sein, et al., 1987). The importance of organizational support for EUC, in promoting EUC success, is highlighted in our results by the positive correlations of EUC support with end-user attitudes, and with all the indicators of EUC success. This emphasizes the need for EUC support through the Information Center (IC) concept as suggested by Carr (1987), Igbaria & Chakrabarti (1990), Magal, et al. (1988), and Nelson (1989). The IC

286

Impact of end-user computing on the individual

concept appears very instrumental for EUC success as it improves the access to, and utilization of, EUC resources through ongoing end-user training programs which (a) identify informational needs across the entire organization, and (b) promote favorable attitudes toward EUC. In effect, an IC enables the entire organization to tap the power of the computer relatively expediently, and can ultimately lead to increased EUC success. Here, the management implication is that active "management support" should be accorded to EUC support mechanisms like Information Centers and related activities. Our results showed that the attitudes toward EUC had significant positive effects on all the EUC success indicators. Specifically, the end-user attitudes toward EUC were found to be strongly correlated with the subsequent use of the system. This finding supports previous studies that have stressed the importance of end-user attitudes in making EUC more successful in organizations, and in creating a successful EUC environment (Amoroso, 1988; Cheney, et al., 1987; Rivard & Huff, 1988). With regard to the impact of on the individual end-user, our results indicate that EUC had some effects on individuals, specifically on their job satisfaction and quality of work-life (QWL). Job satisfaction was found to be positively correlated (see Table 2) with number of tasks, time of use and frequency of use. This finding is consistent with the speculations by Cheney and Dickson (1982) and Counte, et al. (1985). However, job satisfaction was unrelated with the end-user satisfaction with the system, and with number of applications used. On the other hand, as seen in Table 4, we found that the EUC success indicators had significant effects on job satisfaction. These results have major implications for both research and practice, since greater job satisfaction is expected to decrease absenteeism and turnover, enhance organiza­ tional commitment, and ultimately increase productivity (Baroudi, 1985; Bartol, 1983; Kaye & Sutton; 1985). For research, the variety and complexity of the variables involved in this EUC-productivity connection emphasize the need for a systematic research examination of the consequences of using EUC systems in a work setting. In practice, the financial importance of this link between EUC and productivity implies that, when making investment decisions related to EUC, management should not overlook the possible impact of proposed EUC systems on organizational productiv­ ity and profitability Interestingly, EUC success had positive effects on quality of work-life (QWL). While this result contradicts the finding of those who see computer technology as degrading jobs in general (Kraut, et al., 1989), it is still reasonable to expect that EUC increases quality of work-life specifically in the EUC environment. This is because of the greater autonomy, variety, and increased challenge (in terms of individual skill and accuracy), which EUC brings to office work (Millman & Hartwick, 1987). Evidence suggests that, in addition to decreasing absenteeism and turnover (Baroudi, 1985; Bartol, 1983; Bartol & Martin, 1982; Cheney, 1984; Couger & Zawacki, 1980; Hackman & Oldham, 1980; Millman & Hartwick, 1987), such perceptions on the part of office workers are associated with an increase in the individual's motivation,

Impact of end-user computing on the individual

287

commitment, and productivity (Coates, 1988; Kaye & Sutton, 1985; Kraut, et al., 1989; Oglivie, et al., 1988; Porter, 1987), in other words, the enrichment of jobs and work. At the same time, this positive impact could also be related to favorable attitudes toward EUC among the end-users. In our results, we found that the end-users who have favorable attitudes toward EUC were more likely to report higher levels of job satisfaction and QWL. This result is consistent with the attitude theory propositions (Fishbein & Ajzen, 1975), in that it may be the individual's initial attitudes toward EUC that actually lead to the reality of the EUC success, and eventually to the variety of positive effects on the end-user's job and work as observed above. It should be noted that, in our results, increased QWL was also associated with greater organiza­ tional support for EUC as perceived by the end-users. In conclusion, this study extends the existing research on end-user computing by focussing specifically on the determinants of EUC success, and on their impacts on jobs and work. The results obtained demonstrate that the jobs and work in the EUC environment are impacted by the level of EUC success, and by the end-user attitudes toward the systems they use. Furthermore, the EUC success indicators were themselves found to be affected by certain individual-level and organizational-level characteristics. Overall, these results provide a moderate support for the proposed model involving various relationships among the study variables. It should be noted, however, that in this study we did not control for certain variables, such as, the system type, task structure, management style, and job productivity. Given the use of MBA part time students, the possibility of common method variance in accounting for the results is acknowledged. Additional research using multiple data sources, as well as more objective measures of system-type, task-type, and career outcomes, could provide increased confidence in the results obtained. Finally, research involving pre- and post-installation observations of jobs, and quality of work-life, is needed for determining whether any of the predicted changes had actually occurred in practice.

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