Examining User Resistance and Management Strategies in Enterprise System Implementations Tim Klaus
Stephen Wingreen
J. Ellis Blanton
Texas A&M University – Corpus Christi 6300 Ocean Dr. Corpus Christi, TX 78412 (361) 825-2379
Southeastern University 1000 Longfellow Blvd. Lakeland, Florida 33801 (863) 667-5000
University of South Florida 4202 E. Fowler Ave, CIS1040 Tampa, FL 33620 (813) 974-6757
[email protected]
[email protected]
[email protected] ABSTRACT
Although user resistance has been identified as one possible form of non-adoption by the technology adoption literature [33], it is unclear whether user adoption and user resistance are at opposite ends of the same continuum, or whether they are separate concepts that focus independently on positive and negative aspects of system implementation and usage. It seems reasonable to expect that, in any given system environment, not all system users have adopted the system willingly, and not all non-users have resisted. Furthermore, research about the relationship between project management strategy and user resistance is practically non-existent. Therefore, it seems necessary to undertake research to develop an understanding of the nature of user resistance that may subsequently serve as a basis for research concerning both effective management strategy and whether user adoption and user resistance are separate parts of the same phenomenon, or different concepts altogether that deserve separate treatment.
This paper is an initial investigation of the management strategies best suited to address user resistance. Despite its relationship to adoption, little is known about user resistance. User resistance is investigated in the enterprise systems (ES) environment because the complexity and richness of ES leads users to manifest the full range of resistant behaviors and beliefs. The Q-methodology revealed eight naturally-existing types of ES resistance and the management strategies preferred by each respective group. The results have implications for both research in the field of user resistance and adoption, and practitioners involved in system implementation.
Categories and Subject Descriptors J.4 [Computer Applications]: Social and Behavioral Sciences.
General Terms
In response to a growing awareness that system users are not homogeneous in their approach to adoption, the technology adoption research has begun to explore the existence and identity of different groups of system adopters. For example, Jurison [16] found that perceptions of technology and adoption rates vary among types of users. Zhang and Han [34] also examines different types of users and found that there are differences among stereotyped groups. Ranchhod and Zhou [23] identifies sets of user patterns among Internet users. Furthermore, Chen and Chen [6] derive profiles of types of users in a recommendation system.
Management, Design, Human Factors.
Keywords User resistance, management strategies, ERP Implementation, resistant behaviors
1. INTRODUCTION The link between user adoption and successful systems implementation has been a topic of interest to both researchers and managers [i.e., 8,33]. One study found that more than half of system projects fail [20] while another found only a 16% success rate [13]. User resistance is one important cause of this, with user resistance considered to be “at the root of many enterprise software project failures” [14, p. 1]. Furthermore, a report on 186 companies that implemented a large system found that resistance is the second most important contributor to time and budget overruns and is the fourth most important barrier to implementation [7].
The Enterprise System (ES) environment has two key characteristics that make it fertile grounds for an investigation of user resistance and distinguish it from most other system implementations. First, ESs require mandatory usage throughout the organization. This is necessary since the system integrates data to produce organizational reports useful for managers; these reports would not be very useful if only some departments used the system while others entered data elsewhere. A second characteristic is that business processes are reengineered to match the processes required by the system rather than revolving the system around the way the business runs. A clear reason for and benefit of ES implementation is efficiency gains through process reengineering; these changes are made during the system implementation and affect the users.
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theories focus more on behavioral intention and the cognitive processes rather than actual behavior. Furthermore, studies that have applied some of the user acceptance models to mandatory settings have found mixed results. For example, both Brown et al. [3] and Venkatesh et al. [32] examine mandatory usage, but Venkatesh et al. [32] finds significant relationships that support TAM while Brown et al. [3] failed to support parts of TAM.
A two-part research question is proposed to understand the types of user resistance and associated management strategies in the systems implementation environment with respect to the unique characteristics of their respective viewpoints. What types of user resistance occur during ES implementations? a) What are the characteristics of these naturally occurring types of resistance?
Another characteristic of the ES environment that contributes to user resistance is the reengineered processes. ESs are not needed for reengineering, but one main benefit of an ES is the process reengineering that occurs as the technology is implemented. Employees can be greatly affected by the job transformation caused by the ES implementation. This transformation is often difficult, as found in Alvarez and Urla (2002), which suggests that users have values, work habits, and dilemmas that carry over and challenge the new system. This readjustment usually causes a temporary reduction in performance (Hitt et al. 2002), and unresolved resistance can cause a much greater problem (Jiang et al. 2000). Standardized modules are used and often only partially customized in order to minimize initial implementation costs and future upgrade costs. Managers are discouraged from making modifications to these standardized modules unless they are absolutely necessary since every initial customization performed also requires customization for every upgrade. Rather, managers generally choose to reengineer processes to make them more efficient, often requiring changes in reward structures and job tasks. Ross and Vitale (2000) describes how resistance took place in many forms since some users’ jobs significantly changed, some lost power, and most had to unlearn as well as relearn. Essentially, an ES implementation requires organizational change, which often alters the tools, skills, rewards, tasks of the job, organizational structures, and even beliefs and values.
b) What are the management strategies identified by these types that will be most effective in minimizing the level of resistance? There are several contributions of this study. One contribution of this study is a better understanding of why users resist an ES, the behaviors exhibited by users, and management strategies to minimize user resistance. A second contribution is the understanding of types of ES users, such as an understanding of how user resistance manifests itself through behaviors among groups of users. There has not been any research found that has been conducted in this area. Yet, an understanding of types of users, and in particular, resistant groups, is key to understanding how to mitigate user resistance. This research explores the area of resistant groups and their characteristics of these groups, and sets the groundwork upon which future theories can be built. A third contribution is an understanding of the management strategies most desired by users and perceived to be the most important in minimizing the level of user resistance. Users provided feedback regarding the management strategies desired during the implementation. This research will proceed first by reviewing the literature on user resistance and management strategies, and the ES environment. According to the methods and procedures prescribed by Concourse Theory, a concourse on the subject of user resistance and associated management strategies will be defined as a basis for developing a set of Q-sort items that will be administered to a representative sample of ES users. Data will be gathered and analyzed using the Q-sort and Q-factor analysis procedures. The interpretation of the data will reveal naturally-existing types of user resistance and their respective preferred management strategies.
Furthermore, not all ES user resistance is negatively affects reengineering efforts. Sometimes, valuable feedback can be obtained through an examination of user resistance, particularly as it pertains to the preferred management strategies of resistors. It is possible both for managers to learn healthy means to address resistance and for ES users to add value to the success of the ES implementation. Hence, it would seem that both the level of voluntariness and the context of reengineering processes that occurs with the ES affects user resistance. Although the previously mentioned studies examine mandatory adoption, none do so in an ES environment, which by nature involves both mandatory usage and the transformation of jobs. Both researchers and practitioners would benefit from a better understanding of the impact of user resistance on system success.
2. LITERATURE REVIEW 2.1 User Resistance to an ES There is an extensive body of research that has focused on system acceptance in voluntary settings. For example, the Technology Acceptance Model (TAM) [8,21,28] and the Unified Theory of Acceptance and Use of Technology Model [33] have their roots in the context of voluntary adoption. Studies using these models have consistently found relationships among beliefs, attitudes, behavioral intentions, and usage behavior, and typically focus on the initial decision about whether or not to use a system.
2.2 Management Strategies to Address User Resistance
However, an important distinction between the context of most of these studies and the context of an ES implementation is that usage is almost always mandatory for ES users rather than voluntary. Since the theories noted above were developed in the context of voluntary adoption to explain the acceptance of an innovation, they are limited in their ability to explain user resistance in a mandatory context. Also, studies using these
Whether or not the employees are aware of the effects of their user resistance, managers and system implementers must still address the issue in a manner that produces favorable results. Aladwani [1] discusses the need for management to proactively and constructively deal with user resistance rather than reacting when it arises. However, this requires management to understand the nature of user resistance and take appropriate steps, such as
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appropriately marketing the ES to employees [1]. It is important for management to have strategies in place to minimize the negative effects of user resistance. Without adequate strategies, it is quite possible for management to errantly search for resisters, punish the compliers, and promote the uninvolved. The other extreme would be for management to take no action to address resistance, which could also lead to problems.
3. METHODOLOGY 3.1 Q-Methodology, and Q-sort In the context of this study, Q-methodology and the Q-sort are prescribed by Concourse Theory to reveal the various types and structure of user resistance and the management strategies naturally evident in the communicability among ES users (Stephenson, 1978; 1986a; 1986b). States of communicability are operationalized by the participants themselves as they arrange the Q-sort statements according to their own unique points of view (Stephenson, 1986 – 1988; Stephenson, 1988). Since each statement is sorted in relationship to all others in any given sort, a Q-sort represents the individual’s coherent point-of-view on the subject. A Q-factor analysis correlates individual points of view into factors that represent the types of viewpoints on the subject, and hence reveals the naturally-existing structure of discourse (Stephenson, 1986 – 1988; Brown, 1980). As such, Qmethodology stands at the intersection of qualitative and quantitative research methods – a characteristic that makes is possible to appropriate the most effective aspects of both approaches.
Few IT studies have examined management strategies to address user resistance. Jiang et al. [15] examines management strategies and identifies twenty general strategies that are based on resistance literature. Managers can use this list of general strategies as a checklist for various types of systems. However, as with most checklists, all the items are not applicable to every environment; this is demonstrated by the conclusion of Jiang et al. [15] that the system type affects the management strategies employed.
2.3 Concourse Theory Concourse Theory proposes that the natural structure of discourse in a domain exists in states of complementary communicability [25,26]. Any given state of communicability is defined by the shared communications or beliefs of its members. For example, an analysis of the debate on the subject of how to train MIS Ph.D. students revealed three distinct perspectives held by faculty members [29]. The three perspectives are each defined by the shared beliefs of the MIS professors who constitute that particular perspective, and as such represent a “state” of beliefs about how best to train MIS Ph.D. students. Collectively, the states define the structure of discourse in the domain.
The most significant difference between Q-methodology and more common survey and questionnaire methods is that all elements of a Q-sort are dependent on all others. On the other hand, one of the basic assumptions of a questionnaire or survey is that each measurement is made independently of all others. This substantially differentiates the Q-methodology and Q-sorting from cluster analysis and profile analysis, its closest relatives among correlational analytical methods, despite the claims that some make to the contrary (Brown, 1980; Thomas and Watson, 2002). The interdependence of elements in a Q-sort is the operational means by which each individual’s coherent point-ofview is captured (Brown, 1980). In theory, similar results could be obtained by cluster analysis if all respondents were to somehow agree amongst themselves as to how they ranked with respect to each other on each measurement before they recorded their individual measurements.
Previous research, however, has largely ignored the encompassing significance of Concourse Theory, preferring instead to focus almost entirely on Q-methodology and the Q-sort, which are the method and measurement prescribed by Concourse Theory [25,26,27]. There are several such examples of IS research that employ either the Q-methodology or the Q-sort. IS researchers have used the Q-sort to establish the existence of organizational subcultures and their relationship to DSS user satisfaction [18], competencies of software engineers [31], attitudes of systems analysts [11], and metaphors in the language of IS that may be used to increase the effectiveness of systems development [17]. The Q-methodology has also been employed to investigate managerial decision-making about the deployment of technology throughout the firm [30], relative importance of IT management issues [12,22], comparing information systems between organizations [10], and compare the work climate of an IS organization to those in other industries [24].
3.2 Instrument Development and Data Collection The Q-sort instrumentation was developed according to the guidelines delineated by previous research (Thomas and Watson, 2002; Brown, 1980). The concourse was defined using transcripts of a focus group of 11 ES users and thirty-four individual interviews of ES users from three different organizations which each lasted an average of 45 minutes. The focus group and interviews revolved around the experiences the users had during the ES implementation and sought to understand the reasons for user resistance, the resistant behaviors exhibited, and the management strategies used to minimize user resistance. A representative sample of twenty-nine Q-sort statements was drawn from the transcripts to represent reasons for user resistance and resistant behaviors, and eight statements to represent the management strategies preferred to minimize user resistance (see Appendix). The Q-sorts for each respondent were combined and then the whole was later analyzed using centroid Q-factor analysis. Following previous research, respondents also provided explanations of their reasons for sorting the statements as they did
Since Concourse Theory offers a philosophy for how to interpret the results of a Q-sort and its subsequent Q-factor analysis, any study that focuses entirely on Q-methodology or Q-sort to the neglect of Concourse Theory potentially suffers from a threat to the interpretation of the results. In the context of this study, the full range and richness of user resistance is expected to be manifested on account of the richness of the ES implementation environment. Concourse Theory predicts that this richness will be manifested in the communicability of ES users.
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management strategy from ES users; and 3) domain experts revising the Q-sort instrument.
(Brown, 1993), thus providing qualitative information to assist with the interpretation of the Q-factor analysis. The data collection consisted of a convenience sample drawn from organizations participating in 317 ES user groups. An email was sent to three user group listserves seeking representatives who would be willing to participate in the study by agreeing to distribute the questionnaire to 15-20 users within their organization. Only organizations that had rolled out a system less than three years ago were included in the data collection. A total of 24 representatives from these user groups agreed to distribute questionnaires. All but two of these representatives received a packet of 20 questionnaires and business reply envelopes along with instructions on distributing the questionnaires. The other two representatives received the questionnaire via email. Several weeks after sending out the questionnaire, a follow-up email was sent to each of these representatives. It was thus determined that a total of 354 Q-sort instruments were actually distributed to ES users, 128 of which were returned, demonstrating a 36.2% response rate from ES users who actually received the questionnaire. However, since a total of 480 questionnaires were sent out to user group representatives, there was a 26.7% response rate to questionnaires that were sent out.
4. ANALYSIS Response bias was examined through a comparison of two sets of subsamples: early vs. late respondents and respondents vs. nonrespondents, based on respondent demographics. Bias related to the ordering of the Q-sort statements was examined by randomizing the order of Q-sort statements for two different versions of the instrument and comparing the two groups with ttests. These procedures revealed that there were no significant differences between any of these groups. The Q-sorts were analyzed using PQMethod, software that is often used to analyze Q-sort data. PQMethod provides comprehensive statistical support for Q-method studies, including choices of principal components analysis, factor analysis, and related analytical options. Following previous research, the analysis proceeded using centroid Q-factor analysis with varimax rotation (Thomas and Watson 2002; McKeown and Thomas 1988). Centroid Q-factor analysis extracts factors based on an analysis of the betweenperson correlation matrix. Since the factors are formed from inter-correlated groups of Q-sorts, each factor represents a “type” of person. Inasmuch as the varimax rotation method produces orthogonal factors, it maximizes a function that evaluates both the differentiation between user types and the homogeneity within user types. In the context of this study, the procedure defines types of user resistance that are both internally consistent and unique in their relationships to one another.
3.3 Reliability and Validity The importance and role of validity in Q-methodology research has been the subject of a growing volume of debate. Construct validity, and hence also convergent and discriminant validity, as they are usually understood do not apply to Q-methodology, since Q-methodology focuses on the correlation of people rather than the variables. Brown [4] points out that individuals’ responses are at issue rather than the operational definition of variables, and thus “The concept of validity has very little status [in Qmethodology] since there is no outside criterion for a person’s own point of view” [4,, p. 174-175]. Although it has been suggested that a comparable Q-analysis and R-analysis suggests some degree of validity [2], most Q-methodology research has treated construct validity as irrelevant since the methodology is striving to understand the relative points-of-view of respondents. Dennis [9] points out that the reliability and validity of Qmethodology lies in the data rather than the measure and that “ascertaining construct or predictive validity are inappropriate and irrelevant” [9,, p. 413]. In this respect, Q-methodology is related strongly to qualitative research for its assumption that there is no substitute to a respondent’s point of view [9]. Lincoln and Guba point out that in obtaining a person’s viewpoint, “since there can be no validity without reliability, a demonstration of the former [validity] is sufficient to establish the latter [reliability]” [19,, p. 316].
Factors were retained for further examination based on three criteria: eigenvalues, interpretability of the factor, the researchers’ judgment over whether the factor contributes significantly to our understanding of user resistance and associated management strategy (Thomas and Watson 2002; McKeown and Thomas 1988; Brown 1980). Generally speaking, a factor is retained if its eigenvalue is greater than one, although Brown [5] notes that it is not the absolute cut off value in the selection of factors. It is also desirable that a factor be both interpretable and theoretically significant, as determined by the researchers’ best judgment, because it is possible both to extract a statistically significant factor that is uninterpretable and theoretically insignificant, and to fail to extract a theoretically significant factor using purely statistical criteria [4,, p. 43]. Eight factors were selected for detailed analysis, which explained a cumulative 58% of the variance. Eight factors were selected for two reasons: 1) there was a slightly larger gap between the eigenvalues of the eighth and ninth factors than there was between the other factors; and 2) eight is a sufficient number of groups to analyze, since the purpose of this research is to identify the main groups that form from the data analysis, not to explain every group/factor that exists. The eight factors identified were then rotated using a Varimax rotation, commonly used with Qmethodology studies to identify the factors that maximize the amount of variance.
Previous research has demonstrated that the highest probability of obtaining a representative sample of Q-sort statements is accomplished by deriving the statements from: literature on the subject, interviews with people involved with the concourse, and feedback from domain experts regarding the content of the Q-sort instrument [9]. These criteria were satisfied in this study by: 1) generating a set of Q-sort statements based on a review of literature and interviews with domain experts; 2) obtaining feedback from participants in the concourse of user resistance and
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rotation. For the analysis of the factors, the concourse statements that were most representative of the user groups’ ES experience were identified (-3 is the most representative of their experiences, +3 is the least representative of their experiences). The highlighted factors in Table 1 are the top third of concourse statements that respondents indicated were representative of their ES experience. The responses greatly varied depending on the group and are individually described below.
5. RESULTS The results are reported according to Thomas and Watson [29], which recommends that the analysis and interpretation of the Qsort should contain: 1) Factor loading arrays; 2) normalized factor scores; 3) the statement(s) on which arrays load. With this information, the readers are able to check and reinterpret the researchers’ logic, thus minimizing any errant effects of the researcher’s judgment on the interpretation of factors. Table 1 below shows the factors that were identified using the PQMethod software’s centroid factor analysis with Varimax
Table 1: Centroid Q-Factor Analysis of User Groups Concourse Statement REAS-Uncertainty REAS-Lack Input REAS-Lose Control REAS-Self Efficacy REAS-Changed Job REAS-Workload REAS-Technical Problems REAS-Environment REAS-Lack of Fit REAS-Communication REAS-Training REAS-Complexity BEH-Challenged BEH-Dont Follow Processes BEH-Shadow System BEH-Old System BEH-Avoid BEH-Inappropriately BEH-Hack BEH-Refusal BEH-complain BEH-Defensive BEH-Demotivated BEH-Less productivity BEH-Impatient BEH-Quit BEH-Dont want to learn system BEH-Turnover Intention BEH-Procrastinated MGMT-Communication MGMT-Feedback MGMT-Provide Support MGMT-Training MGMT-Incentives MGMT-Clear Plan MGMT-Expertise MGMT-Customizations
1 -0.91 -1.58 -0.91 0.80 -1.75 -1.98 -0.85 -0.29 -1.01 -0.55 -0.53 -0.14 -0.09 0.61 0.07 0.05 0.65 1.00 1.19 1.79 0.30 0.75 0.20 0.48 1.10 0.16 0.55 0.29 0.72 -1.13 -1.63 0.64 1.30 1.74 -0.81 -1.25 1.02
30 34 29 8 36 37 28 24 31 26 25 23 22 13 20 21 11 7 4 1 16 9 18 15 5 19 14 17 10 32 35 12 3 2 27 33 6
2 0.29 -0.54 -1.01 0.28 -1.03 -0.01 0.29 0.79 0.80 -0.06 1.30 1.13 -0.80 -1.05 -1.83 0.33 0.84 -0.54 -1.04 -1.02 1.38 0.02 0.79 0.00 -0.03 -0.49 1.34 0.57 -0.81 0.01 -0.02 0.87 -1.85 -0.82 -1.83 1.60 2.13
15 25 30 16 32 20 14 11 9 23 5 6 27 34 36 13 8 26 33 31 3 17 11 19 22 24 4 12 28 18 21 7 37 29 36 2 1
3 -0.79 -0.80 -0.29 -0.38 -0.45 -1.56 -0.96 -0.81 -1.30 -0.23 -0.70 -1.44 0.12 0.61 0.61 0.67 0.95 0.37 1.15 0.78 -0.48 0.35 -0.06 0.87 -0.25 1.53 1.16 1.33 0.05 1.05 -1.14 -1.06 -2.03 0.82 -1.06 1.69 1.69
27 28 22 23 24 36 30 29 34 20 26 35 17 13 14 12 8 15 6 11 25 16 19 9 21 3 5 4 18 7 33 31 37 10 32 2 1
4 1.00 -0.54 1.28 -0.06 -0.29 -0.95 -1.44 -1.28 -1.96 -1.63 -0.84 -0.96 -0.20 0.37 0.85 -0.23 0.40 0.25 0.87 1.24 0.47 0.17 -0.14 0.72 -0.56 1.51 0.94 0.89 0.09 -0.36 1.17 1.39 -0.17 1.00 -2.08 -1.57 0.67
7 27 3 20 25 30 33 32 36 35 29 31 23 16 11 24 15 17 10 4 14 18 21 12 28 1 8 9 19 26 5 2 22 6 37 34 13
5 -0.59 -0.10 -0.76 -0.27 -2.24 -2.07 -0.97 -0.05 -0.92 0.28 0.75 -1.07 0.21 0.93 1.10 0.50 0.67 0.09 0.25 0.36 -0.32 0.27 -0.13 0.03 0.36 1.71 0.60 1.01 0.69 2.00 -0.39 0.79 1.28 -2.03 0.14 -0.27 -1.84
29 23 30 26 37 36 32 22 31 15 8 33 18 6 4 12 10 20 17 14 27 16 24 21 13 2 11 5 9 1 28 7 3 35 19 25 34
6 0.63 0.25 1.09 -1.78 -1.56 -1.10 -1.75 -1.41 -0.77 0.19 0.22 -0.62 -0.01 0.67 0.64 1.01 0.23 1.69 1.23 0.67 0.10 0.67 0.64 0.29 -0.58 -0.12 0.15 -0.72 0.11 -1.64 0.82 0.22 -1.44 -1.48 1.69 0.30 1.47
13 16 5 37 34 30 36 31 29 20 19 27 24 9 12 6 17 2 4 10 23 9 12 15 26 25 21 28 22 35 7 19 32 33 2 14 3
7 1.11 1.39 1.31 -0.53 -1.19 -1.50 -1.90 1.35 -1.41 0.50 1.34 -1.76 -0.22 -0.26 0.00 -0.34 0.49 -0.16 -0.05 0.06 -0.40 0.06 0.73 -0.05 -0.19 -0.05 1.09 0.36 0.87 -1.72 -0.64 -1.05 1.05 2.13 -0.64 0.74 -0.52
Explanation: - REAS is reason for user resistance; BEH is resistance behavior; MGMT is management strategy - For each of the factors, the normalized factor loading is first displayed, followed by the statement ranking
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6 2 5 28 32 34 37 3 33 12 4 36 23 24 17 25 13 21 20 16 26 16 11 20 22 20 7 14 9 35 29 31 8 1 30 10 27
8 0.21 -0.48 -0.96 0.25 -1.42 -0.77 -1.31 -0.88 -1.11 0.29 -0.32 -0.85 0.39 0.40 0.69 0.60 0.48 0.65 0.32 0.93 0.41 0.72 0.13 0.84 -0.58 0.04 1.09 0.04 0.29 1.20 0.67 0.41 -2.19 2.85 0.52 -1.50 -2.05
21 26 31 20 34 28 33 30 32 19 25 29 16 15 7 10 12 9 17 4 14 6 22 5 27 24 3 24 18 2 8 13 37 1 11 35 36
have more expertise in the system and in rolling out the system. The third most desired strategy is better top-down communication.
In type 1, resistant behaviors were not among the top third of concourses selected and is termed “Non-resistant group”. Type 1 identified various reasons for resistance and management strategies to minimize user resistance, but was the only type that did not exhibit any resistant behaviors.
Table 2: Rank Ordering
Type 2 exhibited the most resistant behaviors and will be termed “Resistant group”. Six of the seven behaviors highlighted are active behaviors, with only one behavior that is passive (procrastination). From management’s perspective, type 2 is most resistant. In order to minimize the resistance, the top three management strategies identified by type 2 are training, incentives, and a clear plan.
Z-Score
Concourse Statement
-1.836
REAS-Workload
-1.516
REAS-Lack of Fit
-1.500
REAS-Technical Problems
-1.406
REAS-Changed Job
For type 3, is characterized by the resistant behavior of complaining and is termed “Complainers 1”, as it is similar to type 5 which also ranked complaining as their primary resistant behavior. To minimize the complaining, management can provide better feedback, support, training, and a clear plan.
-1.141
REAS-Complexity
-0.703
REAS-Environment
-0.633
REAS-Lack Input
-0.508
REAS-Communication
Type 4 exhibited only the resistant behavior of impatience as part of the top behaviors identified and is termed “Impatient 1”, since type 4 is similar to type 8 which also exhibited impatience. Type 4 identifies that better communication, a clearer plan, and management expertise would have been the most useful management strategies.
-0.195
REAS-Training
Reasons for User Resistance
Resistant Behaviors
Type 5 is termed “Complainers 2”, since it is also characterized primarily by complaining, as in type 3. Type 5 is distinguished from Type 3 by their belief that training was not poor, and their preference for management strategies involving incentives, expertise, and customizations instead of provision of training, management support, and a clear implementation plan.
-0.078
REAS-Uncertainty
-0.039
REAS-Self Efficacy
0.023
REAS-Lose Control
-0.203
BEH-Challenged
-0.047
BEH-Impatient
0.039
BEH-Complain
0.344
BEH-Old System
0.352
BEH-Defensive
Type 6 identified impatience, turnover intention, and actual turnover (quitting) as the most representative behaviors and is termed “Quitters” because of their intention to quit the job or actual turnover. Type 6 identified management communication, training, and incentives as the most important management strategies that should have been implemented better.
0.359
BEH-Procrastinated
0.391
BEH-Unmotivated
0.578
BEH-Inappropriately
0.594
BEH-Don’t Follow Processes
0.641
BEH-Less productivity
Type 7 is characterized by complaining and using the old system, and is termed “Passive Resisters” because they complain and use the old system rather than confront a problem. Type 7 identified five management strategies and ranked system complexity and technical problems as the top reasons for user resistance.
0.758
BEH-Shadow System
0.758
BEH-Avoid
0.828
BEH-Hack
0.859
BEH-Turnover Intention
Type 8 identified impatience as the top resistant behavior and has been termed “Impatient 2” because impatience was exhibited similar to type 4. However, there were different reasons for user resistance and management strategies.
0.992
BEH-Don’t want to learn system
1.023
BEH-Quit
1.344
BEH-Refusal
In order to examine the reasons for user resistance, the resistant behaviors, and the management strategies in more detail, the Zscores were calculated to show which best characterized the implementation experiences of all the respondents. As shown in Table 2 below, the additional workload was the most significant reason for user resistance, followed by a lack of fit, technical problems, and changed jobs. In regards to resistant behaviors, challenging the management plan was the most representative of ES users’ experiences, followed by impatience, complaints, and then trying to use the old system. For management strategies, a clear concise plan was the most desirable management strategy for users. The second most desired strategy is for the managers to
Management Strategies
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-0.805
MGMT-Clear Plan
-0.680
MGMT-Expertise
-0.227
MGMT-Communication
-0.086
MGMT-Feedback
-0.039
MGMT-Training
0.453
MGMT-Customizations
0.531
MGMT-Provide Support
0.797
MGMT-Incentives
could be due to insufficient statistical power, the implication is that both resisters and non-resisters exist from all demographic
6. DISCUSSION
backgrounds. For example, age, gender, education level, and years with employer do not affect the level of resistance or the user group which best fits an employee.
This study examines the types of users, focusing on the characteristics of users, the types of resisting users, and the desired management strategies identified by each types. Eight types were examined, two of which had a greater degree of resistant behaviors. The existence of resistance types is consistent with previous studies that suggest types of users exist. Due to the lack of other studies examining resistance types, however, it is not possible to compare the results of this study with previous research. This is, in fact, a contribution of this research. This study sets the groundwork by demonstrating both that resistance types exist, and the definition of the respective types.
One potential limitation with using Q-methodology is that Qmethodology is more concerned with the degree to which the concourse is represented rather than the general population of people. The relative proportions of types of people in the general population are usually unknown, since the unit of analysis is the concourse and its factors rather than individuals or groups of individuals. The percentage of variance explained by a Q-factor, therefore, is an indication of the percentage of the total number of Q-sorts that comprise that factor. However, if the respondents are also representative of the general population of people, then generalizations may additionally be made with respect to the relative proportions of each factor in the general population, in this case ES users. Based on both the development and analysis of the Q-sort instrumentation and the characteristics of the respondents in the study, we believe that this research also satisfies the dual criterion, and generalizations may be made about both the concourse and its factors, and the general population of ES users.
From a manager’s perspective, knowing that various types exist in an ES implementation can lead to strategies that better meet the needs of the various types. For example, each of the eight types identified in the results had a different set of reasons for user resistance. To some types, a lack of input was important while to other types, the uncertainty was important. Therefore, depending on the employees, some may want to be on a planning committee while others do not; others need some computer training classes while others do not; and some want to have more top-down communication while others do not mind having only minimal communication.
7. CONCLUSION
In regards to the overall results for the respondents, the management strategies that were shown to be the most desirable to users are a clear plan, management expertise, and top-down communication. Although each type has different preferences, these three were shown to be the most important overall to users. Managers should also be aware of the reasons for resistance that were most often present during implementations. The top five reasons, in order of representativeness to ES implementations, are additional workload, lack of fit, technical problems, changed job, and system complexity. If possible, managers should try to minimize the potential problems that arise from these areas. For example, the problem of lack of fit could be minimized through spending more time to find the best system suitable to the organization and have organizational change management in place to alter any necessary processes prior to the system implementation. There are many other suggestions provided in the “Managing the Reasons for User Resistance” section of this section.
The richness of the ES environment provides a full-bodied picture of user resistance and associated management strategy. This study reveals both the existence and identity of various types of end-user resistance and their respective preferred management strategies. Concourse Theory and the Q-methodology provide theoretical and methodological foundations from which to interpret the meaning and structure of resistance types that are most likely to be encountered by managers, and a foundation for subsequent research on the subject. The results are of interest both to practitioners who are involved in system implementation and researchers involved in user resistance and adoption.
8. BIBLIOGRAPHY
In comparing the results to previous studies, a number of studies have identified user groups. For example, Jurison [16] found that perceptions of technology and adoption rates varies among types of users. Zhang and Han [34] also examines different types of users and found that there are differences among stereotyped groups. Ranchhod and Zhou [23] identifies sets of user patterns among Internet users. Furthermore, Chen and Chen [6] derive profiles of types of users in a recommendation system. However, even though all of these studies identified types of users, they did not examine any aspect of resistance or types of resisters. This study expands beyond previous studies to examine user resistance. Despite the collection of various demographic data, one surprising finding was that there were not any respondent demographics identified that differentiated the groups of users. Although this
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1.
Aladwani, Adel M. "Change Management Strategies for Successful ERP Implementation." Business Process Management Journal 7 (2001): 266-275.
2.
Brouwer, Marten. "Validity: Q vs. R." Operant Subjectivity 16 (October/January 1992-1993): 1-17.
3.
Brown, S.A.; Massey, A.P.; Montoya-Weiss, M.M.; and Burkman, J.R. "Do I Really Have To? User Acceptance of Mandated Technology." European Journal of Information Systems 11 (2002): 283-295.
4.
Brown, Steven R. Political Subjectivity - Applications of Q Methodology in Political Science. Westford, MA: Yale University Press, 1980.
5.
Brown, Steven R. "Q Technique and Method: Principles and Procedures." In New Tools for Social Scientists, edited by William D. Berry and Michael S. Lewis-Beck. Beverly Hills, CA: Sage, 1986.
6.
Chen, Hung-Chen, and Chen, Arbee L.P. "A Music Recommendation System Based on Music and User Grouping." Journal of Intelligent Information Systems 24 (2005): 113-132.
7.
Cooke, Dudley P., and Peterson, William J. "SAP Implementation: Strategies and Results: The Conference Board, 1998.
8.
9.
22. Pimchangthong, Daranee; Plaisent, Michel; and Bernard, Prosper. "Key Issues in Information Systems Management: A Comparative Study of Academics and Practitioners in Thailand." Journal of Global Information Technology Management 6 (2003): 27-44. 23. Ranchhod, Ashok, and Zhou, Fan. "Comparing Respondents of E-mail and Mail Surveys: Understanding the Implications of Technology." Marketing Intelligence & Planning 19 (2001): 254-262.
Davis, Fred D. "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology." MIS Quarterly 13 (September 1989): 319-340.
24. Ryan, Ann Marie, and Schmit, Mark J. "An Assessment of Organizational Climate and P-E Fit: A Tool for Organizational Change." The International Journal of Organizational Analysis 4 (1996): 75-95.
Dennis, Karen E. "Q-Methodology: New Perspectives on Estimating Reliability and Validity." In Measurement of Nursing Outcomes, edited by Ora L. Strickland and Carolyn F. Waltz. New York: Springer, 1988.
25. Stephenson, W. "Concourse Theory of Communication." Communication 3 (1978): 21-40.
10. Dos Santos, B.L. "Information Systems: Similarities and Difference across Organizations." OMEGA 17 (1989): 9-20.
26. Stephenson, William. "Protoconcursus: The Concourse Theory of Communication: I." Operant Subjectivity 9 (January 1986): 37-58.
11. Dos Santos, Brian L., and Hawk, Stephen R. "Differences in Analyst's Attitudes towards Information Systems Development: Evidence and Implications." Information & Management 14 (1988): 31-41.
27. Stephenson, William. "Protoconcursus: The Concourse Theory of Communication: II." Operant Subjectivity 9 (April 1986): 73-96.
12. Gottschalk, Petter. "Key Issues in IS Management in Norway: An Empirical Study Based on Q Methodology." Information Resources Management Journal 14 (April-June 2001): 37-45.
28. Taylor, S., and Todd, P.A. "Understanding Information Technology Usage: A Test of Competing Models." Information Systems Research 6 (1995): 144-176. 29. Thomas, Dominic M., and Watson, Richard T. "Q-Sorting and MIS Research: A Primer." Communications of the Association for Information Systems 8 (2002): 141-156.
13. http://www.standishgroup.com/sample_research/ chaos_1994_1.php. "The CHAOS Report." 14. Hill, Kimberly. "System Designs That Start at the End (User)." CRM Daily Accessed: October 13, 2004 (March 26 2003).
30. Tractinsky, Noam, and Jarvenpaa, Sirkka L. "Information Systems Design Decisions in a Global versus Domestic Context." MIS Quarterly 19 (December 1995): 507-534.
15. Jiang, James J.; Muhanna, Waleed A.; and Klein, Gary. "User Resistance and Strategies for Promoting Acceptance across System Types." Information & Management 37 (2000): 25-36.
31. Turley, Richard T., and Bieman, James M. "Competencies of Exceptional and Nonexceptional Software Engineers." Journal of Systems Software 28 (1995): 19-38. 32. Venkatesh, Viswanath, and Davis, Fred D. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies." Management Science 46 (2000): 186-204.
16. Jurison, Jaak. "Perceived Value and Technology Adoption across Four End User Groups." Journal of End User Computing 12 (October-December 2000): 21-28. 17. Kendall, Julie E., and Kendall, Kenneth E. "Metaphors and Methodologies: Living Beyond the Systems Machine." MIS Quarterly 17 (June 1993): 149-171.
33. Venkatesh, Viswanath; Morris, Michael G.; Davis, Gordon B.; and Davis, Fred D. "User Acceptance of Information Technology: Toward a Unified View." MIS Quarterly 27 (September 2003): 425-478.
18. Kendall, K.E.; Buffington, J.R.; and Kendall, J.E. "The Relationship of Organizational Subcultures to DSS User Satisfaction." Human Systems Management 7 (1987): 31-39.
34. Zhang, Xiangmin, and Han, Hui. "An Empirical Testing of User Stereotypes of Information Retrieval Systems." Information Processing and Management 41 (2005): 651664.
19. Lincoln, Y.S., and Guba, E.G. Naturalistic Inquiry. Beverly Hills, CA: Sage, 1985. 20. "ERP Survey Results Point to Need for Higher Implementation Success." 21. Mathieson, K. "Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior." Information Systems Research 2 (1991): 173-191.
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