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The Impact of Individual Adaptation to IT on User Emotional Reactions

Research in progress1

Christophe Elie-Dit-Cosaque Université de Lorraine

ISAM – IAE Nancy – CEREFIGE 25, rue Baron Louis

54007 Nancy Cedex France christophe.elie-dit-cosaque@univ-lorraine.fr

Jessie Pallud Ecole de Management Strasbourg

HUMANIS 61 avenue de la Forêt Noire

67085 Strasbourg Cedex jessie.pallud@em-strasbourg.eu

Abstract

IS research provides a number of research models that allow understanding individual acceptance and use of systems. However, most of them fail to account such phenomenon as user adaptive strategies to information technology (IT) and their influence on emotions and eventually on IS success. Drawing on the Coping Model of User Adaptation (CMUA) (Beaudry and Pinsonneault 2005) and on the Information Systems Success Model (ISM) (DeLone and McLean 2003), we elaborate a research model for gaining further knowledge into the influence of user adaptive strategies on positive and negative emotions, namely satisfaction and frustration. Two generic adaptive strategies - benefits maximizing and benefits satisficing - are posited to mediate the influences of system quality, opportunity, and perceived behavioral control on satisfaction and frustration about IT use. A quantitative, field survey has been conducted. In total 11 corporations located in France and more than 3400 IT users have participated in the study. The design and the expected contributions for research and practice are presented.

Keywords: Coping Model of User Adaptation, Satisfaction, Emotion, IS success model, System Quality.

Résumé

La recherche en systèmes d’information (SI) offre différents modèles permettant de mieux comprendre l’acceptation et l’usage des technologies de l’information et de la communication (TIC). Toutefois, la plupart de ces modèles ne prend pas en considération des phénomènes tels que les stratégies d’adaptation des utilisateurs et l’influence de ces stratégies sur les émotions et, au final, sur le succès de l’implantation des TIC. En nous fondant sur le Coping Model of User Adaptation (Beaudry et Pisonneault, 2005) et sur le IS success model (Delone et McLean, 2003), nous élaborons un modèle de recherche visant à améliorer nos connaissances sur l’influence des stratégies d’adaptation individuelles sur les émotions positives et négatives, la satisfaction et la frustration en particulier. Deux stratégies génériques, la « maximisation des bénéfices » et la « satisfaction passive concernant les bénéfices attendus de la TIC » sont posées comme des variables qui médiatisent l’influence de la qualité du système, de l’opportunité représentée par la technologie, du contrôle comportemental perçu, sur la satisfaction et la frustration relatives à l’usage des TIC. Pour tester ce modèle, une enquête quantitative a été conduite avec 11 organisations situées en France. Plus de 3400 utilisateurs d’un progiciel de gestion intégrée y ont pris part. La conception de l’étude ainsi que les contributions attendues pour la recherche et la pratique sont présentées.

Mots clés : coping, satisfaction émotions, qualité du SI, modèle du succès des SI.

1 Cette étude a bénéficié du soutien financier de la Fondation CIGREF, ISD Program, vague C. L’étude a également bénéficié de l’appui de l’USF, le club des utilisateurs SAP francophones, qui a permis l’accès au terrain. Nous remercions la Fondation CIGREF et l’USF pour leur soutien.

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INTRODUCTION

Understanding the determinant of the success of Information Systems (IS) projects has been a topic of interest for many IS researchers and practitioners. The fact is that several studies (Bashein and Markus 1994; Lyytinen and Robey 1999) and reports, suggest that the rates of large IS implementation failure are still very high. For example, a study has been conducted in France and found that approximately only one third of IS projects were successful (ANACT 2009). Among the most important factors in the success and failure of IS projects, it has been suggested that change management and user involvement are among the most important ones (StandishGroup 1995; Nelson 2007; ANACT 2009). Interestingly, however, the role of emotions in this process has been neglected in the literature, which often assumes that users respond rationally to IT change and disruptions. As IT usage is frequently mandated, users often have very little discretion on it. In fact, they are expected to adapt to the IT. In this sometimes effort-intensive process, emotions are likely to be an important factor of the acceptance or rejection of IT, hence of IS success or failure. Accordingly this research examines the influence of individual adaptive strategies to IT on positive and negative emotions in the course of system usage.

Researchers developed several models to better understand IT acceptance and adoption. Research in this field has, however, largely neglected individual adaptation to information technologies (IT) (Beaudry and Pinsonneault 2005; Benbasat and Barki 2007). In fact, the decision process leading to IT adoption has received scant attention (Bagozzi and Lee 1999). We hence need to better account for the role of individual adaptive strategies to IT in shaping individual responses. The purpose of this research is thus to examine the extent to which system quality and individual adaptive strategies (Beaudry and Pinsonneault 2005) influence user emotions, namely 1) satisfaction, a measure reflecting IS success (DeLone and McLean 2003) and 2) frustration, a measure reflecting the inability of users to reach expected results with the technology.

Several concerns have been raised and are important to address. First, by neglecting user adaptation, prior research creates a black box of system usage. If we could open this black box, we would certainly gain better and more in-depth knowledge of users’ interactions with IT (Benbasat and Barki 2007; Elie-Dit-Cosaque and Straub 2010). Adaptation, which has been defined as “Change in behavior of a person or group in response to new or modified surroundings” (American Heritage Dictionary) helps taking into account a range of behaviors related to IT use, which includes an agency perspective (Beaudry and Pinsonneault 2005). Differently, IT Acceptance research has been adapted to various systems and usage contexts in order to improve extent conceptualizations of individual responses to system implementation. For example, researchers studied user adoption of hedonic information systems (Van der Heijden 2004; Wakefield and Whitten 2006), systems for which usage is mandated (Brown, Massey et al. 2002; Hwang 2005), e-commerce systems (Pavlou and Fygenson 2006), or e-government systems (Teo, Srivastava et al. 2008). Few, however attempted to provide models that include agency behaviors during and after the assessment of implemented IT.

A relevant adoption model is the IS success model (DeLone and McLean 1992; DeLone and McLean 2003). That model explains how IS can provide benefits to organizations (Rai, Lang et al. 2002; Teo, Srivastava et al. 2008). It posits quality variables as antecedents to adoption and subsequently to IS success. However, neither this model, nor models based on the Technology Acceptance Model (TAM) (Davis 1989; Davis, Bagozzi et al. 1989) include agency behaviors. Hence, they may fail to explain IS success when the technology is complex (Gallivan 2001), or when, for example, the social context of the implementation shows important power and politics enacted by individuals (Crozier and Friedberg 1977; Vaast and Walsham 2005; Avgerou and McGrath 2007). This is often the case with technologies, such as ERP that provoke disruptions in individuals work environments. By applying CMUA we expect to fill in this gap. The research questions we address in this paper are the following:

• To what extent do system quality, IT perceived opportunities and perceived behavioral control influence individual adaptive strategies?

• How do individual adaptive strategies influence users’ emotions in the course of system usage?

The structure of the paper is as follows. First, we emphasize some of the challenges raised by the examination of individual adaptation to IS. We then propose a model for understanding the determinants of individual

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adaptation and its impacts on emotions towards the technology. Following this, we propose a research design to test the model. Finally, we review the main expected contributions of this research.

INDIVIDUAL ADAPTATION TO IT

Beaudry and Pinsonneault (2005) offered an interesting integrated model for understanding user adaptation to IT. Their model synthesizes key findings from both the process and variance traditions of research. Based on the theory of coping (Lazarus and Folkman 1984), CMUA (Beaudry and Pinsonneault 2005) helps acknowledging individual responses to IS, depending on 1) threats and opportunities originated by newly implemented IT, and on 2) the level of control over the self, IT and work (Shaw and Barrett-Power 1997; Beaudry and Pinsonneault 2005). According to that model, when an individual has control over the situation, that individual has the capacity to exploit the benefits of the IT (the individual adopts a Benefits Maximizing adaptive strategy) or to mitigate its negative consequences (the individual adopts a Disturbance Handling adaptive strategy). Conversely, when an individual does not have control over the situation, that individual will benefit from the positive consequences of the IT without further efforts (the individual adopts a Benefits Satisficing adaptive strategy) or attempt to preserve him / herself from the negative consequences (with a Self-Preservation adaptive strategy). Thus, CMUA suggests that individuals act in different ways when new IT are implemented. Adaptation, and subsequently adoption can thus be considered to be dynamic processes instead of static processes.

RESEARCH MODEL

The model given in Figure 1 below is explained hereafter.

Figure 1. Research Model

End-User Emotions: User Satisfaction and Frustration

User satisfaction is a subjective measure of system success (Ives, Olson et al. 1983). In contrast, user frustration reflects some of the downsides effects of IT. Indeed, the IT may embed operating processes that constrain individual action, or simply not providing users the means to solve work issues, thereby generating frustration and stress. Numerous studies specifically investigate the dimensions of user information satisfaction and its measurement (Doll and Torkzadeh 1988; Doll and Torkzadeh 1991; Torkzadeh and Doll 1991; Doll, Xia et al. 1994; Doll, Ziaodong et al. 2004). Prior researchers suggested that there are important differences between system usage as indicated by users through self reports, and actual system use (Straub, Limayem et al. 1995). For that, when system usage cannot be objectively measured, user information satisfaction may be a better measure of system success. It has indeed good face validity and it is a measure that can be assessed through self reports (Ives, Olson et al. 1983). User satisfaction plays important role in technology acceptance and use (Wixom and Todd 2005). Prior research demonstrated that the more satisfied users are with IT, the more they intend to use it. Therefore, those satisfied users are more likely to benefit from the IT features that can help increase individual efficiency and performance.

Opportunity System Quality

User Satisfaction

Perceived Behavioral Control

H1a Benefits Maximizing

Benefits Satisficing

H2a

H5

H4

H6

H8

H7 User

Frustration

H1b

H2b

Emotions

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Individual Adaptive Strategies

The coping model of user adaptation suggests that when confronted with disruptive IT events, individuals engage in adaptation efforts that may influence IS outcomes (Beaudry and Pinsonneault 2005). Beaudry and Pinsonneault (2005) identified four main generic coping strategies of users, including two when the technology is believed to bring new opportunities: benefits maximizing and benefits satisficing. We expect that the adaptive strategy that will be implemented by users will determine the IS performance outcome. According to CMUA (Beaudry and Pinsonneault 2005), individuals adopt Benefits maximizing (BM) coping strategies when the IT has many opportunities, and when they feel they have sufficient control over the IT. BM is a problem-focused adaptive strategy. This means that an individual responding with such strategy will be focused on the IT problem at hand, and will make everything possible in order to directly tackle it. So doing, users will be more satisfied and this will contribute to IS success, which improves work practices (Nelson 2007). A user with a BM strategy will get a good sense of how IT capabilities can be leveraged according to work tasks. It has also been demonstrated that active user behaviors, which may somewhat reflect user commitment, will induce more positive user reactions to system characteristics (Joshi 1991; Nelson 2007)

H1a: User likeliness to adopt a benefits maximizing adaptive strategy to IT will positively impact end user satisfaction.

H1b: User likeliness to adopt a benefits maximizing adaptive strategy to IT will negatively impact end user frustration.

In contrast, Benefits Satisficing (BS) is an adaptive strategy that is implemented when the technology presents opportunities, while the individual does not feel having the capability to effectively cope with the change created by IT. Hence, that individual will attempt to benefit from some easily accessible opportunities. So doing, they also modify their expectations about IT so that they become consistent with their own capability to face the rising IT dilemmas. Meanwhile, however, they are not likely to get the most from the technology and to be satisfied with it. Therefore, we expect that the propensity of users to adopt a BS strategy is negatively related with satisfaction and performance. We hence posit:

H2a: User likeliness to adopt a benefits satisficing adaptive strategy will negatively impact user satisfaction.

H2b: User likeliness to adopt a benefits satisficing adaptive strategy will positively impact end user frustration.

System Opportunities

System opportunities represent the extent to which IS are believed to have the potential to contribute to individual performance. According to Beaudry and Pinsonneault (2005), recognizing opportunities in a given IT contributes to users’ adopting a strategy oriented towards exploiting those opportunities. In such contexts and when users feel they have control over the technology, they are more likely to adopt an active adapting strategy, namely a benefits maximizing one rather than a passive adaptive strategy We hence posit:

H4: Perceived IT opportunities are positively related with the likeliness for the user to adopt a benefits maximizing adaptive strategy towards IT.

H5: Perceived IT opportunities are negatively related with the likeliness for the user to adopt a benefits satisficing strategy adaptive strategy towards IT.

System Quality

System quality is one of the key system characteristics determinants of IS success following Delone and McLean (2003). It refers to features such as flexibility, integration, reliability and ease of use. System quality is related to user friendliness and ease of use of a system (Doll and Torkzadeh 1988b; Rai et al. 2002). Subsequently it is expected to determine the opportunities perceived by users. Hence:

H8: System quality is positively related with system opportunities.

We explain the design and methods for our research next.

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METHODS

Research Design and Setting

A field survey (Pinsonneault and Kraemer 1993) was conducted in France with users of Enterprise Resource Planning (ERP) systems. All SAP implementations cannot be assumed to be the same. Though, ERP qualifies well for being considered as a disruptive technology: they indeed often involve radical changes for the organizations that implement it and for the workers who use it. In fact, ERP often accompany business process reengineering initiatives; rather than adapting an ERP to its specificities, an organization will adapt its work processes and structures to the ERP. ERP systems are also well-known for their ambivalent outcomes. On the one hand they are integrated solutions that help companies save money and time, especially by ensuring information quality, facilitating tasks coordination and work efficiency, and improving control. Thus, some users report being satisfied with their ERP application. On the other hand, ERP systems are often criticized by users for their rigidity or the lack of flexibility, difficult usage and tenuous tasks accomplishments. The rules embedded in ERP systems tend to systematize work tasks making users feel constrained. Furthermore, the use of ERP is often mandated in companies (Straub et al. 1995), which may also reinforce user frustration feelings. The characteristics of ERP can therefore provoke both positive and negative emotions. Consequently, ERP systems present a good fit according to our research hypotheses. They indeed offer many opportunities to examine user perceptions of system quality, opportunities, individual adaptation strategies and our two positive and negative emotions.

We also expected to obtain a wide variety of situations, with respect to threats / opportunities perceptions and to the level of control each worker has over work, self, and IT (Beaudry and Pinsonneault 2005). For these reasons, we believe that the ERP is an appropriate setting for our investigation.

The ERP users who participated in the study were enrolled via our access to and cooperation with the French SAP club. This professional association gathers companies located in France that have implemented one of the SAP modules. After gaining access to this field, we started communicating about our research project, its main principles and objectives during Fall 2012. Simultaneously, we created a working group with IS managers in charge of SAP deployments from different member companies who were interested in the project. These managers relayed information in their own companies. They were in charge of diffusing our questionnaire among SAP users. In fact, our approach was consistent with Rosemann and Vessey’s methodology (2008). Those researchers recommend establishing partnerships with practitioners in order to improve the relevance and utility of research for organizations. Specifically, they propose the concept of "applicability checks" that is, while maintaining a solid conceptual anchor in the research, to develop the design of the research in collaboration with professionals and to validate the practical relevance of research instruments. Our working group was composed of two researchers and a dozen of professionals. We met every two months to interact and build the questionnaire. These interactions have enabled to refine the questionnaire to fit better with business issues (i.e. ERP functionality, coordination, adaptation, user support, etc.), while ensuring scientific rigor and academic relevance. During Spring 2013, we launched the online survey and made several recall to increase participation rate. The last step of our research is the presentation of the results in October 2013 and providing a synthesis to the participating companies. Our research design is summarized in Figure 2 below.

Figure 2. Planning of our research design

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Measures

Measures of system quality are from Rai et al. (2002). User satisfaction is a two item reflective construct from Wixom and Todd (2005). Measures for user coping strategies of adaptation were based on the descriptions made by Beaudry and Pinsonneault (2005), and adapted from Elie-Dit-Cosaque and Straub (2010). In doing so, we also considered other studies from social psychology that already developed such measures in other contexts (e.g., Skinner and Brewer 2002). As users were in an ongoing use context (in contrast to CMUA, which is designed to be tested when the individual learns about a new application), before asking the questions about individual adaptation, we asked the SAP users to answer the questionnaire considering that they had just learnt about unexpected opportunities from SAP. Perceived behavioral control measures are from Venkatesh (Venkatesh 2000), Items were measured on 3 points Likert scales. We included several control variables namely, age, gender, threats and opportunity perceptions (Beaudry and Pinsonneault 2005), the time spent in the company, the number of months of experience with SAP. Measures for coping strategies of adaptation are adapted from (Elie-Dit-Cosaque and Straub 2010).

Sampling

In total, eleven companies operating in different industries such as chemicals, aerospace, cosmetics, banking, personal services, research, public administration and the construction sector participated in our study. Respondents were invited to participate in the survey via an online questionnaire. In order to minimize response bias and subject apprehension, anonymity was guaranteed (Podsakoff, MacKenzie et al. 2003). Regarding participation, 5324 users started the survey but 3334 users finished and submitted their questionnaire. The questionnaire was developed in French and English. This allowed the survey to be deployed in France, but also, in other countries for some companies that have subsidiaries abroad. The final sample is composed of 53.3% women and 47.7% men. Although 77% of participants correspond to end user, other categories are also represented: our sample includes 17% of key users, and the remaining 6% are project managers who participated in the implementation of SAP.

People from all hierarchical levels participated in the study including top executives (who represent 6% of our sample). However, managers represent the main group of our sample, with 45% of them having answered to the questionnaire. Our sample is also characterized by its maturity. Indeed, 75% of the participants have been working in their company for more than five years, and 35% have even worked in the same company for more than 20 years. The functional departments that participated the most in the survey are in order of importance: controlling (15.99%), production (14.03%) and accounting (10.94%).

The main results of the study will be presented at the conference (the analyses are ongoing and not already completed).

EXPECTED CONTRIBUTIONS

This research will have several important contributions for research and practice. First, it will allow improving research on user contribution to IS success. We are confident that by further examining user adaptation and its emotional outcomes, we will enrich and improve our knowledge about non rational responses to IT usage. Also, practitioners will better understand why systems that may be viewed as offering opportunities to users may fail to be introduced into work practices to the fullest extent possible.

Second, this research will contribute to further investigating user coping strategies of adaptation when individuals learn about disruptive IT events (Beaudry and Pinsonneault 2005). In doing so, we adopt a rigorous, applied quantitative approach that may also help increase external validity of CMUA Beaudry and Pinsonneault (2005) and further works (Elie-Dit-Cosaque and Straub 2010). Having a better understanding of user adaptive strategies to IT that disrupt work practices, and of the resulting emotions, practitioners may thus gain knowledge about the key factors they can leverage in order to foster adoption of systems.

This research will thus have important contributions to both research and practice. IS researchers are indeed increasingly concerned about the tendency to focus on a small number of user behaviors during IT implementation (Benbasat and Barki 2007). Also, probably because they want to avoid taking risks with new models or measures, researchers often neglect other more complex behaviors such as user adaptation or reinvention (Benbasat and Barki 2007). Furthermore, research with an over simplified view of system usage, may provide results that are very different from what is found within organizations (Straub, Limayem et al. 1995; Burton-Jones and Straub 2006; Straub and Burton-Jones 2007). By examining the role of system quality on individual adaptive strategies and the impacts of the later on positive and negative emotions, we thus expect to help improve our knowledge of adoption and success of IT.

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CONCLUSION

Beaudry and Pinsonneault (2005) suggest an interesting model of user adaptation that is based on the theory of coping (Lazarus and Folkman 1984). Their approach focuses on users’ adaptive strategies to IT and goes beyond a view that beliefs about the IT itself directly influence individual behaviors. We integrate insights from CMUA and ISM in order to understand how user adaptation influences satisfaction and frustration. We presented a research model and the field material collected will allow testing that model. The results are expected to help practitioners better understanding apparently contradictory user behaviors that may be explained by their adaptive strategies and non rational responses

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