An examination of its impact on end-user satisfaction

End-user computing strategy: an examination of its impact on end-user satisfaction

Rita Moore

ABSTRACT

Organizational attitudes and expectations regarding end-user computing (EUC) have changed radically in the past 25 years and have researchers describing end-user computing as a vital component of the overall information resource in the organization. Throughout this period of unprecedented growth from limited desktop computing to near-saturation desktop and mobile EUC, companies have struggled to formulate appropriate EUC strategy and researchers have suggested that the development of an effective EUC strategy “may be the most important short-term decision the organization can make if it hopes to benefit from its investments in end-user-based technologies” (Alavi, M., Nelson, R. R., and Weiss, I. R., 1987-88, p. 29). Using the EUC Strategy Grid proposed by Munro, Huff, and Moore (1987-88), this research explores the issue of EUC management by examining (1) the relationship between EUC strategy and end-user satisfaction, and (2) the influence of end-user satisfaction with organizational satisfaction. The results indicate that organizations can increase the level of satisfaction of employees engaged in EUC activities by adopting an EUC strategy high in expansion tactics and that the level of dissatisfaction experienced by higher-level end-users can be decreased by avoiding or modifying the containment EUC strategy, characterized by high control and low expansion. Additionally, the EUC strategy can be expected to have a positive influence on user behavior.

INTRODUCTION

Organizational attitudes and expectations regarding end-user computing (EUC) have changed radically in the past 25 years. Initially, EUC was perceived as a departmental-level management issue for MIS. From 1982 until 1991, MIS managers consistently ranked “the facilitation and management of end-user computing” in their lists of top twenty issues (Ball and Harris, 1982; Dickson, G. W., Leitheiser, R. L., Wetherbe, J. C., and Nechis, M., 1984; Brancheau and Wetherbe, 1987; Niederman, F., Brancheau, J. C., and Wetherbe, J. C., 1991). During that same period, as large numbers of organizations made the transition from centralized mainframe technology to decentralized desktop technology, spending for end-user computing in some organizations increased from between 40% and 50% of the computing resources (Rockart and Flannery, 1983) to between 60% and 80% of the IT budget (Amoroso and Cheney, 1991). In less than 10 years, however, EUC had spread so broadly throughout most organizations that it could no longer be considered a management issue solely for MIS managers (Reed, 1989). In 1992, research by Harrison and Rainer confirmed that end-user computing had emerged as a vital component of the overall information resource in the organization. EUC in some organizations was consuming nearly 90% of the computing resources (Amoroso and Cheney, 1992). Increased funding translated to greater numbers of end-users. In a 1994 survey, Nord and Nord found that 98% of those interviewed used a computer in their jobs. Today, end-user computing is part and parcel of the work place; moreover, EUC is now expanding beyond the confines of the office. One writer recently used the term “explosion” to describe the ever growing number of end-users, freed from their desktops by wireless connectivity, engaged in mobile EUC activities (Saran, 2006).

From the beginning, end-user computing has changed the way people worked, improving the collection and organization of data, and allowing them to focus on their basic job responsibilities. At first, early organizational expectations for EUC were primarily to expedite the entry of data into the organization’s centralized mainframe system and to facilitate personal productivity by providing mostly word processing and/or spreadsheet application software on the desktop. By 1990, Boyer suggested that the organization had to achieve a better understanding of end-user computing because it presented such important advantages and disadvantages in areas of time, cost, and quality. Today, in their fast-paced, global environment, businesses actively seek employees with increased technical skills and knowledge, and expect these end-users to utilize the technology for the maximum benefit to the organization (Jawahar and Elango, 2001).

Throughout this period of unprecedented growth in end-user computing, from limited desktop computing to near-saturation desktop and mobile EUC, companies have struggled to formulate appropriate EUC strategy. As early as 1983, while studying the status of end-user computing in corporate America, Rockart and Flannery were surprised to find that the organizations participating in their study did not have a strategy for the management of EUC. The authors suggested that organizations would be required to establish appropriate strategies for the development and management of EUC if they were to take advantage of its immense potential. Since that study, other researchers have suggested that the development of an effective EUC strategy “may be the most important short-term decision the organization can make if it hopes to benefit from its investments in end-user-based technologies” (Alavi, M., Nelson, R. R., and Weiss, I. R., 1987-88, p. 29). A study conducted under the auspices of The Institute of Internal Auditors Research Foundation revealed that only 31 percent of the organizations surveyed had developed their end-user computing in a systematic fashion (Rittenberg and Senn, 1993). In a commentary appearing in Computerworld in 1995, de Jager stated that companies had little to show in the way of increased productivity for the billions of dollars being spent annually on computers, and that the fault rested with the management (or lack thereof) of end-user computing. Reminiscent of Rockart and Flannery’s (1983) findings, de Jager (1995) found that most businesses still had no formal EUC policies, guidelines, or audit procedures to monitor the productivity of their EUC resource.

Rockart and Flannery (1983) recommended that EUC strategy be clearly defined for and between the MIS staff and the end-user community, and that the strategy defined should emphasize the development and management of an environment shared by MIS professionals and end-users. They also suggested that new corporate policies pertaining to such areas as system cost justification and software procurement and development be formulated. History has proven these suggestions to be sound. Today, end-user computing is evolving into end-user development (EUD), which can be a source of either “covert” end-user activity (Ferneley, 2007) or a bone fide application software acquisition alternative for the organization (Martin, E. Wainright; Brown, Carol; DeHayes, Daniel; Hoffer, Jeffrey; and Perkins, William., 2005). Effective strategic planning and responsible information resource management now demand that businesses have an “explicit” end-user computing strategy (Martin et. al., 2005).

Building on the findings and recommendations of Rockart and Flannery, Munro, Huff, and Moore (1987-88) presented a model of organizational EUC strategy defined along two dimensions: expansion and control. The expansion dimension deals with the level of encouragement or support provided by the organization to end-users to increase EUC. The control dimension deals with the extent of limitations or restrictions placed on end-users by the organization to restrain EUC. The Strategy Grid is shown in Figure 1. Since the identification of Munro et al.’s (1987-88) EUC strategies, no research has been located which further investigates EUC management in terms of these two dimensions, although the model continues to be presented in texts as a guide to end-user computer strategy (Martin et. al., 2005).

Using the EUC Strategy Grid proposed by Munro et al., this research explores the issue of EUC management by examining (1) the relationship between EUC strategy and end-user satisfaction, and (2) the influence of end-user satisfaction with organizational satisfaction.

LITERATURE REVIEW

End-Users and End-User Computing

A review of the literature yields several definitions for “end-user.” Cotterman and Kumar (1989) narrowly define end-users as people who interact with computer-based information systems only as consumers of information. Turban’s (1993) definition, on the other hand, is much broader and includes all managers and professionals using PCs, secretaries using word processing, and CEOs using e-mail. Turban (1993) suggests that the end-user can be at any level in the organization or in any functional area. The research presented here uses the broadest possible definition of end-user to include not only consumers of information, but anyone in the organization who directly uses a computer in the performance of his or her job. MIS professionals (i.e., systems analysts, programmer/analysts, and programmers) are excluded from this definition.

A variety of definitions for “end-user computing” have also appeared in the literature. Rockart and Flannery (1983) very simply define end-user computing as computing developed and operated by the user. Turban (1993) defines end-user computing as the development and use of computer-based information systems by people outside the formal IS areas. The definition adopted for this study has been advanced by several authors who define end-user computing as the direct, hands-on use of computers by anyone in the organization with problems for which computer-based solutions are appropriate (Hackathorn and Keen, 1981; Carr, 1987; Doll and Torkzadeh, 1988).

End-user Satisfaction

DeLone and McLean (1992) suggest that the evaluation of information systems (IS) practices, policies, and procedures requires an IS success measure against which various strategies can be tested, and they identify user satisfaction as one of the six major dimensions or categories of IS success. In their investigation of 100 empirical studies examining some aspect of IS success, Delone and McLean (1992) found 33 studies which used user satisfaction as the measure of IS success. The authors state that user satisfaction is probably the most widely used single measure of IS success for three reasons. First, user satisfaction has a high degree of face validity; it is hard to deny the success of a system which its users say that they like. Second, reliable instruments have been developed to measure user satisfaction and to allow the comparison of results among studies. Third, most of the other measures (i.e., system quality, information quality, use, individual impact, and organizational impact) are poor; that is to say, they are either conceptually weak or empirically difficult to obtain. Raymond (1987) suggests that user satisfaction is the best assessment of system success.

Numerous studies are available linking some aspect of EUC and end-user satisfaction. Rushinek and Rushinek (1986) report over 4,500 end-users’ satisfaction with 17 specific system characteristics. Bergeron and Berube (1988) studied end-user satisfaction with various forms of support and management of the EUC environment. They found that end-users were more satisfied with their microcomputing activities when (1) the organizational microcomputing plan was incorporated in the information systems master plan, (2) there was an information center to support end-user activity, and (3) users had access to a hot-line to solve their microcomputing problems. Igbaria and Nachman (1990) made an exploratory study of correlates of end-user satisfaction with end-user computing. They found that significant positive relationships existed between end-user satisfaction and hardware/software accessibility and availability, computer background of users, user attitudes toward end-user computing, and system utilization. Their results also demonstrated that computer anxiety and user age were negatively related with end-user satisfaction. No significant relationships were found between end-user satisfaction and gender, education, and organizational level. Bergeron, Rivard, and DeSerre (1990) found that specific characteristics of an Information Center (IC) resulted in higher levels of end-user satisfaction. They found that end-user satisfaction increases with the variety of services offered by the IC, the proximity of the IC, and the proportion of the IS budget devoted to the IC. Similarly, Mirani and King (1994) found that end-user satisfaction was higher when information centers provided support that fulfilled more of the users’ needs. Shaw et al. (2003) conducted a study which showed that satisfied and dissatisfied end-users have “different technological frames of reference” towards EUC which affect their expectations of the technology, their interactions with the information center support staff, and their utilization of the technology. Aladwani (2002) conducted a field study to investigate the relationship among organizational actions, computer attitudes, and end-user satisfaction, and found that top management advocacy of end-user computing positively influences end-user satisfaction in public organizations.

Measuring End-User Satisfaction

Methodologically, the measurement of user satisfaction is a problem crucial to information systems research, and user satisfaction has been operationalized in many different ways. Scales developed for the measurement of user satisfaction generally fall into two categories: those which focus on the content of the information system or “the product,” and those which include the organizational support for developing and maintaining the system as well as the system product itself (Ives, B., Olson, M. H., and Baroudi, J. J., 1983). Of the 33 studies included in the meta-analysis reported by DeLone and McLean (1992), six studies used the Bailey-Pearson (1983) instrument, nine studies used other multi-item scales, and 13 studies (over one-third of all the studies cited) used a single question about overall user satisfaction. Other studies have employed single-item rating scales (Edmundson and Jeffery, 1984; Hogue, 1987; King and Epstein, 1983; Langle et al., 1984). Many of the more popular measures of end-user satisfaction focus on end-user satisfaction with individual systems and are unsuitable to assess EUC success from a company-wide perspective (Guimaraes and Igbaria, 1994). Since overall ratings of user satisfaction have proven just as effective as multi-item scales (Rushinek and Rushinek, 1986; Rivard and Huff, 1988), and since the research presented here is neither concerned with user information satisfaction nor end-user satisfaction with a particular application, an overall rating of satisfaction with the EUC strategy seems most appropriate.

End-User Computing Strategy

During the early period of EUC strategy research, some authors narrowly defined EUC strategy only in terms of risks associated with the development of software by end-users, and suggested specific EUC policies enumerating particular tactics to control risks of end-user development (Alavi and Weiss, 1986; Davis and Olson, 1985; Leitheiser and Wetherbe, 1986). In more general terms, however, EUC strategy consists of all processes and approaches adopted by an organization for identifying, assessing, and assimilating end-user technologies into the organization (Alavi et al., 1987-88). EUC strategy is implemented and operationalized through policies and procedures dealing with such critical EUC management issues as resources procurement, application development by users, decentralized support services, and control through line management (Kahn, 1992).

Three major models of EUC strategy are proposed in MIS literature (Alavi et al., 1987-88; Henderson and Treacy, 1986; Munro et al., 1987-88). Brown and Bostrom (1989) characterize all three of these models as evolutionary because they are “based on the assumption that the organization’s EUC strategy should change over time to match the stage of EUC assimilation within the given organization (p. 80).”

Henderson and Treacy (1986) propose a four-stage model for the management of end-user computing. Their model is prescriptive in nature, based on prior literature and not specific field data. The four stages in the Henderson-Treacy (1986) model are Implementation, Marketing, Operations, and Economic. The authors, assuming that the organization’s overall EUC management objective is to maximize the rate of EUC diffusion, suggest that each stage of EUC management requires its own particular structure and set of control mechanisms.

Taking a descriptive research approach based on their study of 47 different organizations, Munro et al. (1987-88) propose a model of four EUC strategies established along two dimensions, expansion and control. Expansion is the rate or pace of EUC growth and development in the organization. Control refers to actions taken to direct the activities and choices made by users. The authors contend that organizational EUC strategy directly affects the rate of diffusion of EUC technologies and the outcomes of EUC activities within the organization. They further suggest that the end-user computing strategy finally selected by an organization reflects which dimension (expansion or control) is more dominant in the organization. A strategy dominated by control results in slow diffusion and limited application of end-user computing, while a strategy dominated by expansion results in rapid diffusion and widespread application of end-user computing.

Munro et al. emphasize that the EUC strategy adopted by an organization has important implications on not only the scope of EUC activities in the organization, but also on the resource requirements. They suggest that the strategy choice made by the organization is influenced by such factors as internal conditions of the firm, observations of the growth patterns of user computing in other firms, and the attitudes of top management. As shown in Figure 1, this model depicts four possible EUC strategies formed when the two dimensions of EUC strategy (control and expansion), each on a scale of low to high, are crossed to form a matrix. The four EUC strategies are defined as follows:

1. Laissez-faire: Laissez-faire, in its extreme, is the “opening position” for most firms, one in which the organization’s interest in end-user computing is low; hence there is no particular drive to increase the amount of EUC in place. Since there is little user computing under way, the need for controls, that is, limitations on EUC choices, is low. Thus, the organization is assuming a “hands-off” posture with respect to EUC.

2. Acceleration: In the Acceleration cell, the firm has decided that it will provide abundant resources for end-user computing but has little concern as to the direction in which EUC will go. The concern is rather to enable each user to have the best possible opportunity to make his or her own decisions regarding solutions for the problems being addressed.

3. Containment: For an organization in the Containment cell, management has decided to develop end-user computing slowly and carefully. The intention is to expand at a low rate and to ensure that the increase in EUC is done in such a way as to remain within precise and narrow growth boundaries defined by the EUC support group or information systems department. Very specific controls are in place and users are required to carry out their computing activities within the limited range of choices these controls permit.

4. Controlled Growth: In the Controlled Growth cell, the organization has chosen to develop end-user computing rapidly, but simultaneously to control carefully the environment in which it occurs. Hence, ample resources are provided to ensure that EUC does in fact take hold and that it is encouraged and well supported. However, at the same time, appropriate controls are in place to ensure that the growth of end-user computing conforms to management’s explicit desires.

The third evolutionary model of EUC strategy is proposed by Alavi et al. (1987-88) and is based on their interviews with five companies. Their model seems to “merge” the two models discussed above by profiling five EUC strategies: laissez-faire, acceleration, monopolist, marketing, and operations-based. The first four strategies correspond with Munro et al.’s (1987-88) laissez-faire strategy, acceleration strategy, containment strategy, and controlled growth strategy respectively. The fifth strategy, operations-based, is included as an “on-going management” strategy (p. 32). In this model, the five core strategies are described in terms of an EUC management framework of policy setting, planning, support, and control similar to Munro et al.’s expansion and control framework. Like Henderson and Treacy (1986), Alavi et al. assume that an organization’s EUC objective in the early stages of their EUC evolution is to maximize the rate of EUC growth.

The model of EUC strategy described by Munro et al. (1987-88) is utilized in this study for several reasons. First, it is based on the largest sample of organizations (i.e., 47). Second, it is the most parsimonious; the two stages (i.e., marketing and operations-based) substituted in the Alavi et al. (1987-88) model for the one controlled growth stage in the Munro et al. (1987-88) model addresses concern for specifying an additional EUC growth stage, not a different strategy. Finally, in their research, Munro et al. developed composite expansion and control indices to provide an objective mechanism for placing the organizations studied into the expansion-control grid. Unfortunately, although significant research concerning the management of EUC has been conducted since the development of the EUC strategy grid by Munro et al. (1987-88), no subsequent empirical research could be found which confirms this model.

Generally speaking, this research studies the relationship between EUC strategy and end-user satisfaction, and asserts that EUC strategy has an influence on end-user satisfaction, and that end-users’ overall satisfaction with the organization is influenced by their satisfaction with the organization’s EUC strategy. Specifically:

[H.sub.1]: End-user satisfaction will be higher for EUC strategies characterized by low control than for EUC strategies characterized by high control.

[H.sub.2]: End-user satisfaction will be higher for EUC strategies characterized by high expansion than for EUC strategies characterized by low expansion.

[H.sub.3]: End-users’ overall satisfaction with the organization is influenced by their satisfaction with the organization’s EUC strategy.

Hypotheses 1 and 3 are suggested by Spector’s (1986) findings that employees who perceive themselves as having comparatively high levels of control over their work are more satisfied, involved, committed, and motivated. Hypothesis 1 is also supported by Bergeron and Berube’s (1988) suggestion that an increase in the number of policies lowers end-user satisfaction with microcomputing because end-users see policies as restrictions in their work. Hypothesis 2 and 3 are suggested by Igbaria and Nachman’s (1990) study which found that significant positive relationships existed between user satisfaction and such high expansion tactics as (1) hardware/software accessibility and availability and (2) system utilization.

This research presumes (1) that EUC strategy affects end-users’ reactions to their work environment as evidenced by their satisfaction, and (2) that end-users’ overall satisfaction with the organization is influenced by their satisfaction with the organization’s EUC strategy. The questions investigated by this research are:

1. What is the relationship between EUC strategy and end-user satisfaction?

2. What is the relationship between end-user satisfaction with the EUC strategy and overall satisfaction with the organization?

This research suggests answers to these questions by examining the relationship between EUC strategy and end-user satisfaction in a variety of organizations, and by examining how end-user satisfaction with EUC strategy correlates with overall satisfaction with the organization. Figure 2 depicts the conceptual model utilized in this research.

[FIGURE 2 OMITTED]

RESEARCH METHODOLOGY

Research Design

This research is a field experiment investigating the relationship between EUC strategy, end-user satisfaction, and overall organizational satisfaction. In order to operationalize EUC strategy, four EUC strategy scenarios were developed, one for each cell of the strategy grid defined by Munro et al. (1987-88). Development of the scenarios is based on empirical research performed by Munro et al. in which they were able to identify specific expansion and control tactics used by organizations in conjunction with their dominant EUC strategy objective. The four categories of expansion tactics suggested by the authors were:

1. flow of information (high or low) to end-users about computing services and products available;

2. cost to users (high or low) for computing technology, training and support;

3. acquisition of new technology (easy or difficult) by end users; and

4. quality and range of services (high or low) available to end users.

The four control tactics suggested by the authors were:

1. end users are required to buy one specific type of technology or choose technology from an approved vendor list;

2. end users are allowed to only read corporate data files;

3. end users are limited to specific software tools; and

4. MIS has veto power over end-user technology acquisitions.

The scenarios developed for this study incorporate these tactics in different combinations to describe the EUC environment at four hypothetical organizations. After the scenarios were developed, ten faculty members (already knowledgeable with the concept of EUC) at a local college were asked to serve as independent raters, assessing each scenario and assigning it to a cell on the Munro et al. grid. The scenarios were classified with an inter-rater reliability of 100%.

Instrument Design

The first section of the research instrument includes respondent demographics of gender, age, educational level, years in current job, whether or not the current job is a management position, extent of computer use in current job, and number of years the individual has used a computer. The second section includes the four scenarios depicting the four EUC strategies. To reduce bias resulting from the order in which respondents read the scenarios, the four scenarios were presented in the questionnaires in any one of the 24 possible combinations. Approximately equal numbers of questionnaires for each of the 24 different combinations were distributed randomly.

Respondents were asked to imagine that they worked in the organization described and to indicate their satisfaction (1) with the company’s rate of expansion and support for EUC (the expansion dimension), (2) with the company’s restrictions over EUC activities (the control dimension), (3) with the company’s overall policy for EUC, and (4) with the company in general. The 5-point Likert scale ranged from 1 (extremely dissatisfied) to 5 (extremely satisfied).

Sample Selection

Respondents were end-users from 12 Tennessee organizations at all levels of end-user sophistication and in as many functional areas of the organization as possible. Although the respondents were not selected randomly from the population of end-users, the sample is not believed to be significantly biased for several reasons: (1) each respondent is a full-time employee of the company; (2) the respondents are from different types of companies; (3) the respondents within each company represent different functional areas; (4) each respondent is an end-user engaged in some level of EUC activity; and (5) respondents were selected by twelve different individuals.

DATA ANALYSIS

A total of 260 questionnaires were distributed; 153 questionnaires were returned, resulting in a 58.8% overall response rate. Demographically, almost two-thirds (63.4%) of the respondents were female, 58.8% were over the age of 39, 54.3% held a Bachelors Degree or above, and 81% had been using a computer for 5 years or more. 49% of the respondents had held their current job for 5 years or more, 54.2% used their computers more than 20 hours per week, and almost one-third of the respondents (32.7%) had management positions in their organizations.

Table 1 shows the means and standard deviations of the overall satisfaction ratings for the four EUC strategies. In order of mean satisfaction level, the acceleration strategy (LC/HE) received the highest satisfaction rating at 3.9281, and the controlled growth strategy (HC/HE) received the second highest satisfaction rating with a mean of 3.6471. The two strategies characterized by low expansion had the lowest mean satisfaction levels. A simple one-way ANOVA and a Fisher’s LSD post hoc test with significance level set at .05 revealed (1) that the end-users’ mean satisfaction with the acceleration strategy is significantly different from their mean satisfaction with each of the other three strategies, and (2) that the end-users’ mean satisfaction for the controlled growth strategy is significantly different from their mean satisfaction for both the laissez-faire and the containment strategies. There was no significant difference between end-users’ mean satisfaction for laissez-faire and containment.

A Cronbach’s alpha coefficient was also computed for each EUC strategy scenario. For acceleration, controlled growth, containment, and laissez-faire, the Cronbach’s alpha coefficients were .89, .80, .86 and .87 respectively. These high alphas (all over .70) demonstrate a high level of reliability for the EUC strategy scenarios (Nunnally, 1978).

Hypothesis 1

Hypothesis 1 is concerned with the main effect of the control dimension of EUC strategy on user satisfaction without regard to the user’s EUC activity level; it predicts that end-user satisfaction will be higher for EUC strategies characterized by low control than for EUC strategies characterized by high control. The two EUC strategies found in the low control cells of the strategy grid are laissez-faire, characterized by low control and low expansion (LC/LE), and acceleration, characterized by low control and high expansion (LC/HE). The two EUC strategies found in the high control cells of the strategy grid are containment, characterized by high control and low expansion (HC/LE), and controlled growth, characterized by high control and high expansion (HC/HE). Before Hypothesis 1 could be tested, it was necessary to compute each respondent’s average satisfaction rating with the two low control strategies as well as each respondent’s average satisfaction rating with the two high control strategies. To test Hypothesis 1, both a paired t-test and a Wilcoxon matched-pairs signed-ranks test were performed comparing the sample mean of respondents’ satisfaction with low control strategies with the sample mean of respondents’ satisfaction with high control strategies. The results of both tests are shown in Table 2. Both tests failed to reject the null hypothesis; therefore, Hypothesis 1 was not supported.

Hypothesis 2

Hypothesis 2 is concerned with the main effect of the expansion dimension of EUC strategy on user satisfaction; it predicts that end-user satisfaction will be higher for EUC strategies characterized by high expansion than for EUC strategies characterized by low expansion. The two EUC strategies found in the high expansion cells of the strategy grid are acceleration, characterized by low control and high expansion (LC/HE), and controlled growth, characterized by high control and high expansion (HC/HE). The two EUC strategies found in the low expansion cells of the strategy grid are laissez-faire, characterized by low control and low expansion (LC/LE), and containment, characterized by high control and low expansion (HC/LE). Before Hypothesis 2 could be tested, it was necessary to compute each respondent’s average satisfaction rating with the two high expansion strategies as well as each respondent’s average satisfaction rating with the two low expansion strategies. To test Hypothesis 2, both a paired t-test and a Wilcoxon matched-pairs signed-ranks test were performed comparing the sample mean of the respondents’ satisfaction with high expansion strategies with the sample mean of respondents’ satisfaction with low expansion strategies. The results of both tests are shown in Table 3. With p-values of .000 and .0000 respectively, both tests rejected the null hypothesis; Hypothesis 2 was supported.

Hypothesis 3

Hypothesis 3 predicts a strong association between end-user satisfaction with the organization’s EUC strategy and overall end-user satisfaction with the organization in general. To test Hypothesis 3, a Pearson’s product moment correlation analysis was performed comparing end-user satisfaction with the EUC strategy in an organization with end-user satisfaction for the organization. A strong correlation, .9022 (p = .000) rejected the null hypothesis; therefore, Hypothesis 3 was supported.

DISCUSSION

This research empirically investigates the relationship between EUC strategy and end-user satisfaction, and the influence of end-user satisfaction on overall organizational satisfaction. Generally, the study expected to find high levels of end-user satisfaction associated with EUC strategies characterized by low control and with EUC strategies characterized by high expansion. Also, the study expected to find that end-users’ overall satisfaction with the organization would correlate with their overall satisfaction with the EUC strategy. Figure 3 summarizes the results of the respondents’ end-user satisfaction ratings with the four EUC strategy scenarios developed for this research by placing the end-user satisfaction sample means previously reported in Table 1 on the EUC strategy grid previously presented in Figure 1.

Hypothesis 1 had predicted that end-user satisfaction would be higher for EUC strategies characterized by low control (i.e., laissez-faire and acceleration) than for EUC strategies characterized by high control (i.e., containment and controlled growth); however, this hypothesis was not supported by the data. Respondents reported their highest overall levels of satisfaction with the two high expansion EUC strategies (i.e., acceleration and controlled growth), and their lowest overall levels of satisfaction with the two strategies characterized by low expansion (i.e., laissez-faire and containment). Respondents’ greater dissatisfaction with the low expansion strategies seems to outweigh their great satisfaction with the low-control acceleration strategy. A review of job satisfaction literature indicates that important factors contributing to job satisfaction include mentally challenging work, equitable rewards, supportive working conditions, and supportive colleagues (Robbins, 1991). Employees are concerned with the work environment not only for their personal comfort, but also for doing a good job; this concern includes having adequate tools and equipment (Robbins, 1991). The level of expansion (i.e., high or low) present in the EUC strategy adopted by an organization impacts the amount of organizational resources available for end-user support and training, as well as the acquisition of new hardware and software technologies as they become available. Literature also suggests that an organization’s policies can contribute to explaining and predicting employees’ attitudes and behavior to the extent that those policies reduce employees’ ambiguity and clarify their understanding of what they are supposed to do and how they are supposed to do it (Spector, 1994). Satisfaction increases when employees experience greater certainty about future directions and outcomes of the organization (Zeffane, 1994). The laissez-faire EUC strategy was described to the respondents in this study as one in which the organization’s overall interest in end-user computing was low and in which the organization’s commitment of resources to EUC was small. The containment EUC strategy was described as one in which the organization’s desire was to move slowly and carefully. In response, end-users in this study indicated their lowest level of satisfaction with these two low expansion EUC environments. The effect of the low level of the control dimension in the laissez-faire strategy was apparently lost in the negativity of this overall low expansion, unsupportive and uncertain EUC environment.

As predicted by Hypothesis 2, end-user satisfaction is significantly higher for EUC strategies characterized by high expansion than for EUC strategies characterized by low expansion. This finding supports an earlier study which found significant positive relationships between user satisfaction and such specific high expansion tactics as (1) hardware/software accessibility and availability and (2) system utilization (Igbaria and Nachman, 1990).

Taken together, the fact that Hypothesis 1 was not supported and the fact that Hypothesis 2 was supported seem to suggest that the expansion dimension has a stronger influence on end-user satisfaction than the control dimension. This may be partially explained by the findings of the well-known obedience to authority experiments conducted at Yale in the early 1960s which concluded that when people are placed in a subordinate role, most relinquish their individual control and defer to the authority structure in place (Milgram, 1963). Since employees relinquish much of their individual control to the organizational authority structure when they accept employment (Rigg, 1992), perhaps their satisfaction with the EUC environment is less influenced by the control dimension of EUC strategy than by the expansion dimension.

As predicted by Hypothesis 3, end-users’ overall satisfaction with the organization is influenced by their satisfaction with the organization’s EUC strategy. As end-users’ level of satisfaction with the organization’s EUC strategy increases, their level of overall satisfaction with the organization increases. Since an organization’s EUC strategy involves aspects of both control (e.g., restricting end-users and EUC activity) and expansion (e.g., providing resources for EUC activity), this finding is consistent with other studies which investigated employees’ satisfaction as influenced by their perception of organizational control and organizational support. Spector (1986) found that employees who perceive themselves as having comparatively high levels of control over their work are more satisfied, involved, committed, and motivated. Igbaria and Nachman (1990) found that significant positive relationships existed between end-user employees’ satisfaction and such high expansion tactics as (1) hardware/software accessibility and availability and (2) system utilization. Since respondents in this study had significantly higher levels of satisfaction with EUC strategies characterized by high expansion than with EUC strategies characterized by low expansion, it is not surprising that their overall satisfaction with the company would be influenced correspondingly.

IMPLICATIONS FOR PRACTICE AND RESEARCH

This research is of interest to both academicians and practitioners. It builds on past EUC research by utilizing the Munro et al. (1987-88) model of EUC strategy in empirical research. It extends the original study by operationalizing the EUC strategies defined by Munro et al. (1987-88) through the development of four scenarios describing the EUC environment in terms of specific, relevant organizational tactics identified in that same research. For academicians, this research fills knowledge gaps about end-user computing and end-user satisfaction by examining the relationship between EUC strategy and end-user satisfaction. For practitioners faced with the decision of choosing and implementing EUC strategy in their organizations, this research offers insight into the identification of successful EUC strategies. The research does not suggest a particular EUC strategy for a particular organization; rather, this research increases our understanding of the impact of EUC strategy on end-users. The results of this study hold several important suggestions for organizational policy making related to EUC activities which could lead to increased end-user satisfaction. First, organizations can increase the level of satisfaction of employees engaged in EUC activities by adopting an EUC strategy high in expansion tactics. Second, organizations can decrease the level of dissatisfaction experienced by higher-level end-users by avoiding or modifying the containment EUC strategy, characterized by high control and low expansion. Based on a study by Gatian (1994) which indicates that a relationship does exist between user satisfaction and user behavior, EUC strategy which increases user satisfaction (or conversely, which decreases user dissatisfaction) can be expected to have a positive influence on user behavior.

The results of this study suggest several opportunities for further research. The research model could be expanded to include other individual characteristics as moderating variables on the relationship between EUC strategy and end-user satisfaction. For example, need-fulfillment theories of job satisfaction generally assume that individuals differ in the outcomes they prefer (or need) to obtain from their jobs, and hypothesize that the relationship between the outcomes received on the job and satisfaction is dependent upon these preferences or needs (Graen, G. B., Dawis, R. V., and Weiss, D. J., 1968). The model could also be expanded to include other job-related factors as moderating variables on the relationship between EUC strategy and end-user satisfaction. Review of management literature reveals an enduring and well-established stream of research on factors contributing to job satisfaction and job dissatisfaction. For example, one study suggests that certain job dimensions (i.e., achievement, responsibility, and recognition) are more important for both satisfaction and dissatisfaction than certain other job dimensions (i.e., working conditions, company policies and practices, and security) (Dunnette, M. D., Campbell, J. P., and Hakel, M. D., 1967). Another study suggests an interaction between end-user computing levels, job motivation, and job satisfaction (Barker, 1995).

CONCLUSIONS

Because end-user computing holds both significant advantages and significant risks for the organization, there is an increased need for organizational EUC strategy. As the level of organizational EUC activities continues to grow, so does the need for EUC strategies containing both elements of control (i.e., acquisition policies and procedures, end-user access to the corporate database, sharing of resources, and quality of systems and information) and expansion (i.e., end-user support and training, and hardware and software availability). Finally, this research suggests that the EUC strategy utilized by the organization not only affects end-user satisfaction but overall satisfaction with the organization. Based on a study by Gatian (1994) which indicates that a relationship does exist between user satisfaction and user behavior, EUC strategy which increases end-user satisfaction (or conversely, which decreases end-user dissatisfaction) can be expected to have a positive influence on end-user behavior.

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Table 1: Parametric and Non-Parametric Test Results For Comparison of

End-Users’ Satisfaction with EUC Strategies Characterized by High

Control and EUC Strategies Characterized by Low Control t-tests

for Paired Samples

t-tests for Paired Samples

Variable Number of Pairs Mean

Satisfaction with High-Control 2.9379

Strategies

Satisfaction with Low-Control 2.9837

Strategies 153

Variable t-value 2-tail sig.

Satisfaction with High-Control

Strategies

Satisfaction with Low-Control

Strategies -.52 .604

Wilcoxon Matched-Pairs Signed-Ranks Test

Variable Mean Rank Sum of Ranks

Satisfaction with High-Control 64.98 3639.0

Strategies

Satisfaction with Low-Control 60.46 4111.0

Strategies

Z = -.5973 2-tailed P = 055.3

Variable Cases

Satisfaction with High-Control 56 – Ranks

Strategies (low control less than high control)

Satisfaction with Low-Control 68 + Ranks

Strategies (low control greater than high control)

29 Ties

(low control equal to high control)

153 Total

Z = -.5973

Table 2: Parametric and Non-Parametric Test Results For Comparison

of End-Users’ Satisfaction with EUC Strategies Characterized by High

Expansion and EUC Strategies Characterized by Low Expansion

t-tests for Paired Samples-

Variable Number of Mean

Pairs

Satisfaction with High-Expansion 3.7876

Strategies

Satisfaction with Low-Expansion 2.1340

Strategies 153

t-tests for Paired Samples-

Variable t-value 2-tail sig.

Satisfaction with High-Expansion

Strategies

Satisfaction with Low-Expansion

Strategies 18.46 .000

Wilcoxon Matched-Pairs Signed-Ranks Test

Variable Mean Rank Sum of Ranks

Satisfaction with High-Expansion 72.01 9722.0

Strategies

Satisfaction with Low-Expansion 29.60 148.00

Strategies

Z = -9.9953 2-tailed P = 000

Variable Cases

Satisfaction with High-Expansion 135 – Ranks

Strategies (low control equal to high control)

Satisfaction with Low-Expansion 5 + Ranks

Strategies (low control less than high control)

13 Ties

(low control greater than high control)

153 Total

Z = -9.9953

Table 3: Overall Satisfaction with EUC Strategy

EUC Strategy Mean Standard Deviation

Acceleration Low Control/ 3.9281 1.0072

High Expansion

Controlled Growth High Control/ 3.6471 1.0789

High Expansion

Containment High Control/ 2.2288 .9767

Low Expansion

Laissez-faire Low Control/ 2.0392 .9168

Low Expansion

Figure 1

EUC Strategy Grid (Munro et al., 1987-88)

High Acceleration Controlled Growth

EXPANSION

Low Laissez-faire Containment

Low High

CONTROL

Figure 3

End-User Satisfaction with EUC Strategy

Acceleration Controlled Growth

High 3.9281 3.6471

EXPANSION Laissez-faire Containment

Low 2.0392 2.2288

Low High

CONTROL

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