ARTÍCULO 4º Definiciones.

Table 3.13. Questionnaire Instrument

Question - Scale Code Wording

Information Quantity [1 - Very Little… 7- A Great

Deal]

QUANT1 How much information do you think was displayed on this website? QUANT2 How much information did you have to process in making your choice? Filtering/Sorting (A) [1-

Not at All… 7 - A Lot]

GU1 How much help did this website provide you with in making your choice? GU2 How guided were you in making your choice?

Use of Sorting / Filtering Capabilities [1- Not at All… 7 - A Great Deal]

SORT1 To what extent did you use the sorting capabilities? SORT2 To what extent did you use the filtering capabilities? Tailored Information [1 -

Strongly Disagree…7- Strongly Agree]

TAIL1 This Website allows me to interact with it to receive tailored information. TAIL2 This website has interactive features which help me accomplish my task. Choice Uncertainty [1 -

extremely uncertain…7 - extremely certain]

UN1 Please rate the level of uncertainty of your choice.

UN2 How certain did you feel making your choice about which MP3 player to buy?

Content [1 - Strongly Disagree…7- Strongly

Agree]

CON1 This website provides the precise information I need. CON2 The information content of this website meets my needs.

CON3 This website displays information that seems to be just about exactly _{what I need.} CON4 This website provides sufficient information.

Accuracy [1 - Strongly Disagree…7- Strongly

Agree]

ACC1 This website is accurate.

ACC2 I am satisfied with the accuracy of this website. Format [1 - Strongly

Disagree…7- Strongly Agree]

FOR1 I think this website is displayed in a useful format. FOR2 The information on this website is clear.

Information Quality [1 - Strongly Disagree…7-

Strongly Agree]

QUAL1 The website provides me with high-quality information.

QUAL2 Overall, I would give the information provided by this website a high _{rating in terms of quality.} QUAL3 Overall, I would give the information from this website high mark. Information Satisfaction

[1 - Strongly Disagree…7- Strongly

Agree]

SAT1 Overall, the information I got from this website was very satisfying. SAT2 I was very satisfied with the information I received from this website.

Intention to Use [1 - Strongly Disagree…7-

Strongly Agree]

IU1 I intend to use this system for making a decision about which MP3 player _{to choose in the future.} IU2 I predict I would use this system for making a decision in the future. IU3 I plan to use this system for my next online purchases.

IU4 If needed an MP3 player in the future, I would probably end up purchasing it on this website.

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Abstract

According to Benbasat and Barki (2007), systems usage has remained a black box in spite of the fact that the construct lies at the heart of a host of studies in the field. Exactly how do users interact with or use systems? Exactly how do they cope with information technology (IT), especially disruptive IT?

First, to answer such questions, we grounded our current work in Beaudry and Pinsonneault’s Coping Model of User Adaptation (2005; CMUA), a model that explains user strategies appraising an IT event. These strategies are a response to threats and opportunities embedded in the IT event and are impacted by the level of control users have over the situation. In the current study, following CMUA, we develop and test measures for a deeper understanding of systems usage and user adaptation to IT through a 2x2 laboratory experiment.

Second, in order to further the latter analysis, this research focuses on the influence of espoused national cultural values (Srite et al. 2006) on user coping strategies of adaptation to disruptive IT. We posit espoused Uncertainty Avoidance (EUA) and espoused Individualism- collectivism (EIC) as individual differences variables that significantly impact user coping strategies to IT implementation.

The model has been tested with 209 undergraduate students from French and US universities. Overall, we found strong support for the CMUA model. Further, the results show that high

EUA individuals tend to adopt problem focused adaptive strategies more than low EUA individuals. We also found that EIC has significant moderating effects on the relation between control and coping strategies in opportunity conditions but not in threat conditions. It shows that more collectivistic individuals tend to be less problem-focused than more individualist ones. The results are then discussed and a future research agenda is proposed.

Keywords: Coping Model of User Adaptation; theory of coping; adaptation; systems usage; disruptive information technologies; threats; opportunities; control; benefits maximizing; benefits satisficing; disturbance handling; self-preservation.

4.1.

Introduction

Implementing IT that is non-disruptive, that is systems that are compatible with previous systems or processes with which the users are already familiar, has long and distinguished history in business. These kind of non-disruptive technologies still offer challenges to managers, but the technology itself is not inherently alien.

The same cannot be said about disruptive technologies. A disruptive innovation is “a novel idea or behavior that, when introduced in organizational settings, causes dramatic changes in the structure of work processes” (Sherif et al. 2006, p. 341). When the technology is “disruptive,” evidence suggests that managers and users do not respond nearly as well. Disruptive IT innovations involve pervasive and radical changes in the organization and in organizational processes (Lyytinen et al. 2003, p. 32).

What are recognizable examples and classes of disruptive technologies? Technologies such as Enterprise Resource Planning (ERP) systems can be considered to be disruptive technologies since the organization is almost always overhauled during the process of implementation (Davenport et al. 1989; Hammer 1990). Therefore, individuals are forced to adapt in different ways, depending on the degree of the disruption caused by the IT. With such large integrated system like ERPs, researchers highlighted many risks of failure leading to undesirable outcomes (Bernard et al. 2004). These risks of failure are linked to the organizational context of implementation, to the system, but also to system user expectations and perceptions of system uncertainty (Larif et al. 2004). For these reasons, it is important to learn how employees adapt to IT in order to better respond to their needs. Also for these reasons, understanding how users adapt to disruptive IT is the main objective of this study.

adaptation to date are either piecemeal or not fully tested, according to Beaudry and Pinsonneault (2005). Moreover, while some researchers have dealt with aspects of the issue, there has been no integrated approach before Beaudry and Pinsonneault’s CMUA, or coping model of user adaptation (2005). These researchers recognized that, although variance or process approaches are each inconclusive in and of themselves, there can be a cumulative effect of studies. Hence, although Beaudry and Pinsonneault (2005) assert this point, they did not themselves attack the question through multi-method approaches. Therefore, additional integrative insights are needed to pull together prior piecemeal views.

To date, variance approaches have not studied user adaptation in depth; most frequently, variance researchers consider it to be implicit in system usage. Pointedly, by not considering how users adapt to technologies, adoption models can lose predictive power when applied to organizations.

How then can a variance approach be utilized to address user adaptation? User adaptation strategies can be modeled as a mediating factor between system attributes and system usage and in this way capture user social embeddedness. Subsequently, this will allow us to better understand the contingencies surrounding use beliefs and use of IT in organizations.

Beaudry and Pinsonneault’s work (2005) is conceptually ground-breaking in that it explicitly models user adaptation. In proposing CMUA, however, they did not develop scales to measure the variables in CMUA and were only able to test it qualitatively with a sample of twelve managers. A key question, therefore, is whether their results will hold when examined through a variance approach and a much larger sample. The basic issue is rectitude: Exactly how good is their model? Second, although the Beaudry and Pinsonneault (2005) model may pass tests in single culture settings, will it be prove to be invariant with respect to user cultural

There is a significant literature that indicates that culture influences system user interactions with respect to IS implementation (Srite et al. 2006; Straub 1994; Straub et al. 1997), but Beaudry and Pinsonneault (2005) do not raise the question of how culture might affect user adaptation strategies. Our research questions are, therefore: (1) Is there empirical support for CMUA (using a variance approach) as applied to the setting of disruptive IT? (2) What influence do user cultural values have on how they adapt to systems?

In order to answer to these questions, the present work: (1) develops and validates an instrument for empirically measuring user strategies of adaptation to IT, (2) shows how user strategies of adaptation to IT can inform user interactions through an enhanced model of CMUA (Beaudry et al. 2005), (3) demonstrates the need to take user strategies of adaptation into consideration in future research related to IT adoption and use, and (4) shows how espoused uncertainty-avoidance and espoused individualism-collectivism influence user adaptive strategies.

We first motivate the need to study user adaptation with a review of pressing major issues remaining in the IS acceptance and use literature. Next, assessing both the variance and process traditions of research in this domain, we discuss how user adaptation has been conceptualized in the past. In this section, we point out the difficulties posed by current piecemeal views of user adaptation. We then model user strategies of adaptation, heavily based on CMUA and the theory of coping (Lazarus et al. 1984), but augmented by the posited influence of cultural values. In the subsequent section are research methods and research design. The design employed was a 2x2 repeated measures, scenario-based laboratory experiment. The final section discusses the contributions and limitations of the work, punctuated by a future research agenda.