User Resistance to IS Implementation in a Mandatory Use Environment

Change ManagementAn International Journal

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User Resistance to IS Implementation in a Mandatory Use Environment

THANACHART RITBUMROONG, UTHAI TANLAMAI, AND KAMALES SANTIVEJKUL

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User Resistance to IS Implementation in a Mandatory Use Environment

Thanachart Ritbumroong, Chulalongkorn University, Thailand Uthai Tanlamai, Chulalongkorn Univeristy, Thailand

Kamales Santivejkul, Chulalongkorn University, Thailand

Abstract:User Resistance to IS implementation has been acknowledged as an important phenomenon frequently cited as a main barrier to the success of system implementation. This paper proposes the user resistance to IS implementation model and tests it with empirical data collected from three large state-owned enterprises in Thailand at different implementation stages. Enterprise Resource Planning implementation was chosen as the context of this study. The perceived level of power in an organization was found to negatively affect user resistance attitude. The results provide implications for both academia and practitioners in the field of user resistance.

Keywords: Resistance to Change, ERP, Mandatory Usage

Introduction

n mandatory usage environment, users are left with no choice but to use the system. They cannot avoid any consequence brought by the newly implemented system. Enterprise Resource Planning (ERP) is a system considered to present a mandated usage environment

(Brown et al. 2002). It is an information system with the aim of helping an organization to improve business operations by integrating all functions along a value chain into one single system (Gupta 2000). Its ultimate goal is, in general, the seamless integration. All business functions will be modeled and linked to create a smooth integration. Ultimately, data will be entered once into the system and shared across the entire organization. This helps to eliminate the problems of data inconsistencies and redundancies. On the other hand, it creates interdependencies among business functions using the same data. If data are not entered correctly or completely, it will disrupt the whole chain of business process. In this case which user tasks are integrated with other users, they are required to use the system in order to support other users’ functions.

Implementing ERP has been proven to take considerable effort. There is on-going concern about the high failure rate of ERP implementation (Kim, Lee, and Gosain, 2005). ERP implementation often requires a substantial amount of resources. When an implementation project cannot follow an original plan, it leads to budget overrun, which can lead to both financial and non-financial loss. The delay of an ERP implemented project can lead to frustration among employees, which can result in an opportunity loss for the organization, and so forth. Furthermore, even after an organization has successfully implemented and deployed the system, it still faces the risk of failing to achieve the objectives of adopting ERP. In a study it was found that over 50 percent of implemented ERP were less effective than original expectations (Yu, 2005). After ERP is deployed, it is not certain whether employees will realize the benefits of using ERP, and eventually they may stop using the system. One such example is Allied Waste Industries, Inc., a Fortune 500 company headquartered in Phoenix, Arizona, that decided to abandon its SAP R/3 after having invested around 130 million U.S. dollars (Kim et al., 2005).

Resistance to change is one common problem that has been addressed in Information System (IS) literature. It was identified as one of critical impediments in ERP implementation (Kim, Lee, and Gosain 2005; Shih 2006; Aladwani 2001). IS researchers have recognized the importance to understand user resistance to IS implementation since it is a critical factor acting as a main barrier to the success of the system. This phenomenon is a normal psychological reaction when

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the perceived consequences (e.g., loss of power) are negative (Ang and Pavri 1994). Lapointe and Rivard (2005) suggested that resistance to IS implementation occurs when an implementation of a new system provokes change altering routine behaviors of individuals. If the change is undesired, an individual will try to maintain the status quo by exhibiting various forms of behaviors ranging from covert activities to aggressive actions. Resistance behaviors can vary greatly from ignorance, negligent, avoidance to aggressive resistance behaviors such as strikes, boycotts, or sabotage etc. Previous studies attempted to explain this so-called phenomenon by identifying various causes of resistance to IS implementation. For instance, Markus (1983) found that perceived threats to power and prestige triggered resistance behaviors. Joshi (1991) postulated that perceived net benefits derived from the IS implementation influenced user resistance. Kim and Kankanhalli (2009) also found that switching costs affected user resistance both directly and indirectly.

While previous IS literature discussed various causes of user resistance to IS implementation, most studies employed qualitative research methodology. There is a lack of quantitative empirical validation to these theoretical concepts (Kim and Kankanhalli 2009). Thus, this study develops a model of user resistance to IS implementation in a mandatory use environment. The model is empirically validated with data collected using questionnaire survey from three large state-owned enterprises in Thailand at different phases of ERP implementation. The results of this study will help verify theoretical understanding about the resistance phenomenon as well as offer practical insights into managing user resistance.

Theoretical Background

Resistance to IS Implementation

Even though user resistance is mostly seen as the opposite continuum of the user acceptance, they should be treated as different phenomena (Bhattacherjee and Hikmet 2007; Cenfetelli 2004). The view that considers user resistance as a separate phenomenon will provide a wider perspective on how to manage users during IS implementation. User acceptance research generally measures the success of IS implementation on intention to use or usage behaviors (King and He 2006). But in an ERP context where system usage is mandatory, it seems to be incomplete to place the usage as a dependent variable. In order for users to commit to system usage, the level of system usage is most likely to provide a limited view of user acceptance. Thus, user resistance research seems necessary to the success of IS in the mandatory usage. Since users have no other alternatives but to use the system, they could easily develop resistance to the new system. This would hinder the IS implementation and lead to budget overrun or delay.

When organization members anticipate an undesirable outcome from a change initiative, they will endeavor to hinder the organizational change process. Like any change initiative, an implementation of an information system is most likely to bring a change into an organization. This change can affect the organization at different levels. At an individual level, a new system can improve job performance. On the other hand, it can be a threat to some individuals. Undesirable outcomes will prompt individuals to impede the implementation process. There are various definitions of user resistance to IS implementation. For instance, Markus (1983) defined it as behaviors intended to prevent the implementation or use of a system or to prevent system designers from achieving their objectives. Marakuas and Hornik(1996) offered an alternative view of resistance as a recalcitrant, covert behavior resulting from both fear and stress stemming from the intrusion of the technology into the previously stable world of the user. In a broader context, resistance can be defined as opposed energies and powers aimed to impede, decline, or stop change for positive or negative purposes (Coetsee 1999). It also can be defined as any efforts or inertia trying to maintain the status quo or hindering change (Val and Fuentes 2003).

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RITBUMROONG, ET AL.: USER RESISTANCE TO IS IMPLEMENTATION IN A MANDATORY USE ENVIRONMENT

Despite the fact that resistance was mostly seen as individuals’ actions or responses, a view that reflected resistance to change beyond individual behaviors could capture the complexity of this phenomenon and provide more understanding about how individuals respond to change (Piderit 2000). The multidimensional view of resistance to IS implementation will reflect individuals’ responses to an IS implementation in both physical actions and mental processes. Thoughts and feelings can be examined to show individual intention to resist the implementation. With a multidimensional approach, the complexity of how users respond to the implementation can be examined in broader meaning. It is reasonable to posit that the attitudinal aspect will allow researchers to understand how individuals form their attitudes which will eventually develop into resistance behaviors. Following the renowned work of Fishbein and Ajzen (1975), the theory of reasoned action and the theory of planned behavior, it is believed that individual attitudes will determine their course of actions. Thus, in this study, resistance to IS implementation is conceptualized as two different constructs; resistance attitude and resistance behaviors.

Theoretical Antecedents of Resistance to IS Implementation

Various causes of resistance to IS implementation have been identified by previous research. Of all the studies focused on the resistance phenomenon, most employed a qualitative research method and identified various causes of resistance behaviors. Given the small number of research in this area, there is scarce empirical evidence of the relationship between causes of resistance and resistance behaviors. Despite the diverse reasons why users resist the implementation, resistance occurs because users perceive threats brought by a system (Klaus, Wingreen, and Blanton 2007; Lapointe and Rivard 2005). In general, users will perceive threats in the situation where they anticipate negative outcomes (Lapointe and Rivard 2005; Martinko, Henry, and Zmud 1996) such as a loss of power (Markus 1983) or a loss of equity (Joshi 1991). Perceived threats can be defined as the degree to which the prospective users consider that the system being implemented will yield negative outcomes. Bhattacherjee and Hikmet(2007) proposed the dual-factor model theorizing the perceived threats to be a determinant of user resistance. The empirical data showed a positive significant relationship between perceived threats and resistance to change.

In this study, two main threats identified from previous studies in IS literature are loss of power (Markus 1983) and loss of equity (Joshi 1991). An introduction of a new system could possibly modify the distribution of power in an organization. Markus (1983) illustrated the case of organization members with a high level of power affected by the change brought by the new system implemented. They were identified as resisters. It could be reasonable to expect that an individual with a high level of power in an organization would be most likely to resist IS implementation. In addition, Joshi (1991) argued that organization members will evaluate fairness derived from changes in their inputs and outcomes. Moreover, they will compare net changes between self and the employer and others. If the result of the evaluation is perceived to be inequity, resistance to change is most likely to occur.

In most cases, a new system requires a new set of skills and knowledge. A lack of capability to use the new system effectively can be a reason for user resistance. This perception is known as self-efficacy. It was found to affect the user acceptance (Calvert 2006). In addition, subjective norm is expected to affect user resistance to change since individuals tend to behave according to the group which they belong to. Individuals may follow their peers and develop resistance attitude and behaviors. Hence, self-efficacy and subjective norm were included into the research framework to verify their influential roles on user resistance to IS implementation.

All in all, there are 5 hypothesized relationships derived which can be illustrated in the proposed framework shown below. Perceived self-efficacy, perceived power, and perceived inequity are hypothesized to influence resistance attitude which, in turn, jointly determine resistance behaviors with subjective norm. The next section will describe how research hypotheses were formulated.

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Figure 1: Research Framework

Power

Markus (1983), in her pioneer work of resistance to IS implementation, evaluated three basic types of resistance theories with data of a single case. Three divergent types of theories are: people-determined theory, system-determined theory, and interaction theory. The underlying assumption of a people-determined theory is that people resist change because of factors internal to them, such as characteristics, cognitive style, and so on. However, a system determined theory suggests technical factors. From the standpoint of this theory, people perceiving a system with technical flaws will resist change. An interaction theory assumes the causes of resistance differently. An interaction between characteristics of people and characteristics of systems causes people to resist change.

Since there are many aspects of interaction theory, Markus’ study (1983) focused only on a political variant caused by an IS implementation. When a new system is implemented, it could alter the distribution of power in an organization horizontally and vertically. When organization members feel the loss of power, they tend to resist the new system implemented. A single case of an implementation of a financial information system was used to validate the aforementioned theories in her study. The comparison between resisters and non-resisters revealed no difference in their cognitive or psychological styles. Although a non-resister was rotated into a position of resisters, resistance did not disappear as predicted by the people-determined theory. The implemented financial system was initially criticized of having technical problems. Later, changes in technical functions were made to resolve the problems. Even though all technical problems were fixed, resistance still persisted. By no means did rotating people or technical improvement reduce resistance since it was found that this financial system modified the power distribution by causing gain and loss of power among groups of employees. It was the political variant that caused the resistance among employees. In the light of the interaction theory, it appeared that this theory better explained events of resistance in this organization. Hence, this research includes this construct into the framework to verify the effects of the each individual’s power level in an organization.

Resistance Attitude

Subjective Norm

Power

Inequity

Resistance Behaviors

Perceived Self-

efficacy

H1

H2

H3

H4

H5

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Hypothesis 1: A level of power in an organization will have a positive direct effect on resistance attitude.

Equity

Joshi (1991) introduced an equity-implementation model (EIM) built upon equity theory, a well-defined theoretical framework concerning judgment of fairness in a social context. He argues that an individual or a user is likely to evaluate the change that the system implementation brings to them at three levels: self, self and the employer, and self and other users. It is believed that the greater the inequity, the greater the distress and vice versa. This model implies that individuals will evaluate most changes before they begin to resist a change. This is to say, individuals will adopt changes and later resist changes perceived unfavorable. At the first level of this model, users determine a net change in an equity status of self by comparing outcomes and inputs required by a new system. They welcome a change with the positive net gain (∆outcomes - ∆inputs) and decline a change which they perceive to be inequitable. Possible inputs regarding an information system implementation include workload, skills, cognitive or mental effort, time, learning and so forth. Possible outputs are job satisfaction, work environment, job security, job performance, power in an organization, etc.

At the second level of this model, users compare relative outcomes of self with their employers since they are likely to feel that the gains should be shared fairly in proportion to expected deservingness of each party. Deservingness is defined as weighted average of outcome expected based upon criteria such as contributions, merit, equality, or other criteria. Users would decline a change if their employer was considered to gain greater relative outcomes compared to them.

At the third level, users would compare self with other users in their reference group in terms of relative outcomes. A perception of fairness would determine an assessment of change consequences. Users would evaluate whether benefits were shared fairly among user groups. When they felt that some groups of users obtained greater benefits even though they had not benefited much, they would not welcome a change and would resist it. Joshi’s (1991) model provides insights into how users evaluate a change in terms of its impact on their equity status. The evaluation of net gains determined by changes in their inputs and outcomes and a comparison between self and the employer and the other users will lead users to resist the change if they perceive the loss of their equity.

In sum, user resistance is expected to be influenced by perceived net benefits gained from the IS implementation. The inequity perceived by an individual would lead to distress and opposition. This research to verify this:

Hypothesis 2: Perceived inequity will have a positive direct effect on resistance attitude.

Self-efficacy

Self-efficacy was considered as an important variable affecting beliefs and behaviors (Igbaria and Iivari 1995). It refers to the comprehensive summary of perceived capability to mobilize the motivation, cognitive resources, and courses of action needed to perform a specific task (Gist and Mitchell 1992). While expectations are believed to be a theoretical underlying foundation of research on user acceptance (Davis, Bagozzi, and Warshaw 1989; Fishbein and Ajzen 1975), self-efficacy was predicted to affect individuals’ outcome expectation. Based on empirical data, Compeau and Higgins (1995a) argued that self-efficacy influenced individuals’ expectations regarding performance outcomes and personal outcomes. Calvert (2006) argued that an ERP user who has a lack of self-efficacy might not accept the system (See also; Compeau and Higgins 1995a; Venkatesh and Morris 2000). This indicates that ERP users with a low level of perceived

49


self-efficacy could develop resistance attitude which would lead them to resist the ERP implementation. Although it could be argued that perceived self-efficacy could be one of resistance antecedents, there have been only a few studies attempting to versify this argument. Thus to examine the role of perceived self-efficacy as an antecedent to resistance to IS implementation, this study hypothesizes:

Hypothesis 3: Perceived self-efficacy will have a positive direct effect on resistance attitude.

Subjective Norm

Subjective norm is defined as a perception of social pressure to perform the specific behavior (Ajzen 1991). Theory of Reasoned Action (TRA) and Theory of Planned Behavior (TPB) theorized subjective norm as a determinant of a behavioral intention. It implies that users will evaluate the judgment of people who are important to them in order to use the system. If these people think that they should use the system, they will be persuaded to do so. Technology Acceptance Model (TAM), which was developed based on these two theories, also included subjective norm into the model in order to explain individual intention to use a system (Venkatesh and Davis 2000; Venkatesh et al. 2003). Users are likely to include judgment of other individuals into account when they evaluate whether to use the system. Similar to user acceptance, subjective norm could possibly lead to user resistance.

Even though a survey of literature in TAM revealed the inconsistencies in the role of subjective norm as the antecedent of intention (Sun and Zhang 2006), studies in the context of a mandatory usage environment found a significant role of subjective norm in a user acceptance process (Venkatesh et al. 2003; Venkatesh and Davis 2000). Generally, subjective norm will influence behavioral intention whether through compliance, internalization, or identification process. According to TAM, subjective norm could influence users to accept the new system. This construct could lessen the degree of user resistance to IS implementation. Thus, it is probable that social influence would lead ERP users to resist IS implementation. Hence, to verify this:

Hypothesis 4: A high level of subjective norm will have a negative direct effect on resistance attitude.\

Resistance Attitude

Following TRA theoretical ground, resistance attitude would be expected to predict resistance behaviors. Fishbein and AjzenFishbein and Ajzen (1975) postulated attitude as a predictor of behaviors in evaluation. Bovey and Hede (2001) found that the relationship between attitude components and intention to resist was significant in their study on organizational change. Irrational ideas and emotion would lead an individual to resist a change. An individual with a high level of resistance attitude would be most likely to express resistance behaviors. Hence:

Hypothesis 5: Resistance attitude will have a positive direct effect on Resistance behaviors.

Research Method

Quantitative data were collected using self-reported questionnaire survey from three large state-owned enterprises in Thailand at different phases; a selection/definition phase, an implementation phase, and an operation phase. POSTAL represents the selection/definition phase. ENERGY characterizes the phase of implementation, whereas WATER had been using

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the system for a certain period and in the operation phase. Data from the three organizations at the different phase were used to validate the five hypothesized relationships in order to develop a user resistance to IS implementation model because we would like to verify the hypothesized relationships whether it would be valid at different organization or phase of the implementation.

Survey instruments were developed based on previous research. Items measuring perceived threats stemming from the impact of power distribution alteration followed the items recommended by Greenhalgh and Rosenblatt (1984) and Ashford, Lee, and Bobko (1989). Perceived threats from the loss of equity were measured by perceived inequitable employment relationship items used in Geurts (1999) and previous studies (Schaufeli, Van Dierendonck, and Van Gorp 1996; Van Dierendonck, Schwartz, and Buunk 1996). Items measuring self-efficacy, developed by Compeau and Higgins (1995b) and used in estimating UTAUT (Venkatesh et al. 2003), were used to assess the degree to which users perceived the level of their self-efficacy. Items measuring subjective norm followed items cited in (Venkatesh et al. 2003). Items for measuring resistance attitude and resistance behaviors were adopted from Oreg’s(2006). All items were measured using a 5-point Likert scale, from Totally disagree (+1) to Totally agree (+5).

Data Analysis

A PLS approach to SEM was chosen to empirically test the proposed hypotheses with the use of smartPLS. The data analysis followed what suggested by Henseler, Ringle, and Sinkovics (2009). All measurement models were assessed for their reliability. Composite reliability and AVE used to assess the reliability and validity of scales in path models from the three cases. All constructs passed the criterion suggested (Composite reliability and AVE should not be lower than 0.6 and 0.5, respectively). Details of the reliability assessment will be furnished upon request. A summary of the latent mean of all constructs is shown in Table 1 to Table 3.

Table 1 Descriptive Statistics: POSTAL

POSTAL: Phase I (N=107)

Composite Reliability

Latent Mean

Latent variable correlations with AVE on the diagonal

PP PI PSE SN RTA RTB

PP 0.904 2.67 0.759 PI 0.824 3.07 0.609 0.544 PSE 0.853 3.41 0.269 0.292 0.745 SN 0.796 3.15 0.190 0.190 0.276 0.667 RTA 0.959 2.42 0.416 0.279 -0.106 0.168 0.769 RTB 0.900 2.48 0.305 0.253 0.055 0.007 0.513 0.751

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Table 2 Descriptive Statistics: ENERGY

ENERGY: Phase II (N=71)


Latent Mean



PP 0.904 2.42 0.761 PI 0.869 3.09 0.478 0.646 PSE 0.821 3.18 0.099 0.378 0.696 SN 0.937 3.10 0.064 0.106 0.202 0.881 RTA 0.929 2.24 0.042 -0.203 -0.426 0.008 0.653 RTB 0.850 2.36 0.177 -0.278 -0.407 -0.067 0.626 0.655

Table 3 Descriptive Statistics: WATER

WATER: Phase III (N=100)


Latent Mean



PP 0.951 2.37 0.867 PI 0.904 2.80 0.702 0.703 PSE 0.911 2.97 0.248 0.351 0.837 SN 0.977 3.03 0.338 0.326 0.331 0.955 RTA 0.941 2.20 0.301 0.192 -0.133 -0.096 0.699 RTB 0.908 2.21 0.272 0.191 -0.179 -0.022 0.519 0.767

Abbreviations: PP = Perceived Power PI = Perceived Inequity

PSE = Perceived Self-Efficacy SN = Subjective Norm

RTA = Resistance Attitude RTB = Resistance Behaviors

Empirical Results: Postal

Only PP was statistically significantly related to RTA (t = 4.146, p < 0.01), whereas, PI and PSE were not a statistically significant determinant of RTA (t = 1.908, p > 0.05 and t = 0.605, p > 0.05, correspondingly). These three constructs jointly explained 22.9% variance in RTA. The relationship between RTA and RTB was statistically significant (t = 5.517, p < 0.01). However, SN was not found to be statistically significantly related to RTB (t = 0.855, p > 0.05). The 26.9% of variance in RTB was explained by RTA and SN.

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Empirical Results: ENERGY

Two of three hypothesized antecedents of RTA were not found to be statistically significant; PP and PI. Only the relationship between PSE and RTA was statistically significant (t = 3.844, p < 0.01). These three antecedents jointly explained 19.8% of variance in RTA. The variance explained seems to be somewhat low. RTB were significantly related to only RTA (t = 6.447, p < 0.01). SN was not found to significantly influent RTB (t = 0.618, p > 0.05).

Resistance Attitude

Subjective Norm

Power

Inequity


-0.081 ns

-0.247 ns

0.526 ** R2 = 0.229 R2 = 0.269

Perceived Self-

efficacy

0.427 **

0.091 ns

* p< 0.01, ** p < 0.05, ns : non-significant at the 0.05 level

Resistance Attitude

Subjective Norm

Power

Inequity

ResistanceBehaviors

-0.072 ns

-0.394 **

0.627 R2 = R2 =

Perceived Self-efficacy

0.139 ns

-0.121 ns

* p< 0.01, ** p < 0.05, ns : non-significant at the

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Empirical Results: WATER

Two of the three hypothesized determinants of RTA were not found to be statistically significant (t = 0.222, p > 0.05 and t = 1.187, p > 0.05 for PI and PSE, respectively). PP was significantly related to RTA (t = 2.579, p < 0.01). There were two hypothesized antecedents of RTB: RTA and SN. RTA was moderately correlated with RTB at 0.01 statistical significanหt level (t = 5.711, p < 0.01), while SN was not statistically significantly related to RTB (t = 0.317, p > 0.05). About 27% of the variance in RTB was explained by these two constructs.

4.4 Summary of the Empirical Results

The summary results from the empirical assessment are shown below. A solid line represents a relationship with consistent results in three cases, whereas a dotted line shows a relationship with inconsistent results. The symbol above the relationship depicts the direction of the relationship (+ is positive, - is negative, ns is non-significant). The majority of the results (two out of three) were reported when the results were inconsistent.

Resistance Attitude

Subjective Norm

Power

Inequity


0.028 ns

-0.229 ns

0.522 R2 =

R2 =

Perceived Self-ffi

0.328 **

0.042

* p< 0.01, ** p < 0.05, ns : non-significant at the 0.05 level

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There are three relationships which the results from the three cases are consistent. The results provide support for only one hypothesis (H4) and do not support other two hypotheses (H2 and H5). Resistance behaviors were affected by resistance attitude (H4). The results of the two hypothesized relationships (H1 and H3) were found inconsistent. Path coefficients, explained variance, and statistical significance from three structural models are shown in Table 4.

Table 4 Summary of structural model path coefficients and explained variance

Hypothesized Relationships Structural model path coefficients

POSTAL ENERGY WATER

H1: PP RTA 0.427 ** 0.139 ns 0.328 **

H2: PI RTA 0.091 ns -0.121 ns 0.042 ns

H3: PSE RTA -0.247 ns -0.394 ** -0.229 ns

H4: SN RTB - 0.081 ns -0.072 ns 0.034 ns

H5: RTA RTB 0.526 ** 0.627 ** 0.522 **

Variance explained in RTA 22.9% 19.8% 13.7%

Variance explained in RTB 26.9% 39.7% 27.0%

Resistance attitude was found to be significantly related to resistance behaviors. This relationship follows what TRA predicted (Fishbein and Ajzen 1975). Resistance attitude tends to be a strong predictor of resistance behaviors. Although subjective norm was expected to increase user acceptance (Venkatesh et al. 2003; Venkatesh and Davis 2000) which would lead to lessen

Resistance Attitude

Subjective Norm

Power

Inequity


Perceived Self-efficacy

H1: +

H2: ns

H3: ns

H4: +

H5: ns

Results were consistent.

Results were inconsistent.

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user resistance to IS implementation, the results of this study show that subjective norm did not directly affect user behaviors in all cases. Kim and Kankanhalli (2009) also did not find direct effects of subjective norm on user resistance. However, they found that this subjective norm could reduce the perception of switching costs and increase the perception of switching benefits, which would influence user resistance. Markus (1983) argued that gain and loss of power among groups of employees during the IS implementation led to user resistance. The results of this research found that perceived power statistically significantly determined resistance attitude in only two cases. It is most likely that individuals with a higher level of power in an organization seem to fear of losing their power as a result of the IS implementation. Thus, they tend to develop resistance attitude towards the system implementation (H1). However, the inconsistent results did not provide full support to the Markus’s (1983) argument. Perceived inequity and perceived self-efficacy, other two predictors, did not appear to affect individuals in developing resistance attitude (H2 and H3). Even though previous research argued that these two concepts influence user resistance to IS implementation (Joshi 1991; Calvert 2006), this study did not find empirical evidences to support the arguments. However, perceived self-efficacy was found to negatively affect user resistance attitude in the case of ENERGY. This provided only partial support to the Calvert (2006) argument. In sum, perceived power in an organization appears to be a good predictor of user resistance attitude. This may owe to the fact that individuals can exert their power in order to bargain or cope with any undesirable. Since IS implementation usually brings changes to the organization structure, users may feel that their power will be altered as a consequence of the implementation. Unlike to perceived inequity and perceived self-efficacy, the loss of power in an organization would be more difficult to be recovered and could lead to other negative consequences such as losing face.

Discussion and Conclusion

User resistance to IS implementation is another stream of IS research viewed as the opposite perspective of user acceptance. This research attempted to empirically validate the antecedents of user resistance to IS implementation. The research framework was derived from previous literature. Four constructs were hypothesized to influence user resistance. Empirical data were collected from three large state-owned enterprises at the different phases of ERP implementation. Perceived level of power in an organization was found to negatively affect user resistance attitude. This finding confirmed what Markus (1983) argued that a political variant caused by an IS implementation could lead to user resistance. This study indicates that users with high power are most likely to develop user resistance as they tend to affect largely by the alteration of power from the ERP implementation. Although perceived inequity and perceived self-efficacy were expected to affect user resistance, this study did not find evidences supporting the claim. This conveys important implications since users tend to hide socio-political oriented conflicts and rather express task-oriented conflicts (Meissonier and Houze 2010). Socio-political oriented conflicts are conflicts due to a loss of power tends or cultural issues whereas task-oriented conflicts are conflicts related to the system, users tasks, and professional skills. Meissonier and Houze (2010) argued that user expression regarding task-oriented conflicts toward IS implementation may conceal socio-political oriented conflicts. This study provides support to this argument since perceived power is the only significant determinant of user resistance while perceived inequity and self-efficacy which could be considered as the consequences of task-oriented conflicts were not found to directly affect user resistance.

Hence, management should take precaution when attempt to introduce mandatory-usage system such as ERP. Under this circumstance, to ask users whether to use or not to use a system would be too simplistic since users are required to use the system. Furthermore, the implementation of ERP would bring typically a radical change to an organization since the system will integrate all disparate business processes and create inter-dependencies among

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departments. This change could threaten users’ status quo and lead them to develop resistance to the implementation. Managing user resistance is a challenge since users may not straightforwardly express their intention. They may conceal their socio-political conflicts and discussed openly only about task-oriented issues (Meissonier and Houze 2010). Management should pay more attention to the socio-political factors in IS implementation rather than focus on the task-oriented problems. Change strategies such as facilitation or negotiation could be used to handle this type of resistance (Kotter and Schlesinger 2008).

In addition, Brown et al. (2002) argued that attitude would play a crucial role in understanding mandated use settings. The results of this study show that resistance attitude significantly determined resistance behaviors in all cases. The attitudinal aspect of resistance to change could provide understanding resistance to change because resistance behaviors might not be able to be directly observed. This suggests that management should not overly focus on behavioral aspects of users since they may hide their resistance attitude as discussed previously. Management should attempt to understand how users think or feel towards the new system.

This research presents some limitations that should be noted. First, the three organizations that served as cases are state-owned enterprises which might provide a particular view of organizations. This may limit the generalizability of the results. Future research might consider replicating this study in another context. Second, there have been no validated items for resistance to IS implementation and some other constructs, particularly negative perceptions or the cause of the resistance. Future research is encouraged to develop scales measuring user attitude. In the present study, antecedents of resistance to IS implementation were hypothesized to include a threat to a power level in an organization, a perception of inequity and perceived self-efficacy. According to Ajzen (1988), an individual could hold a large number of beliefs about an object, but only a few individuals may determine attitude towards an object in evaluation. Future studies should identify salient beliefs about a mandated IS and examine their role in determining resistance attitude.

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ABOUT THE AUTHOR

Dr. Thanachart Ritbumroong: Thanachart Ritbumroong earned a Ph.D. in information technology in business from the Faculty of Commerce and Accountancy in Chulalongkorn University in Thailand. He has experience in change management, business intelligence and data warehouse. His research interests are focused on change management and IS implementation.

Dr. Uthai Tanlamai: Uthai Tanlamai is a professor of Faculty of Commerce and Accountancy, Chulalongkorn University. Most of her research interests deal with the behavior aspects of information systems implementation.

Dr. Kamales Santivejkul: Kamales Santivejkul is a professor of Faculty of Commerce and Accountancy, Chulalongkorn University. He has experience in ERP implementation and IT planning.

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Change Management: An International Journal is one of four thematically focused journals in the collection of journals that support The Organization knowledge community—its journals, book series, conference and online community.

The journal investigates the dynamics of negotiating organizational change, and organizational responses to social, stakeholder and market change.

As well as papers of a traditional scholarly type, this journal invites case studies that take the form of presentations of management practice—including documentation of organizational practices and exegeses analyzing the effects of those practices.

Change Management: An International Journal is a peer-reviewed scholarly journal.

ISSN 2327-798X

User Resistance to IS Implementation in a Mandatory Use Environment

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