Contextualizing Adaptive System Use:
a study on how and why users
revise their system use
by
RENÉE HOLWERDA
University of Groningen Faculty of Economics and Business
MSc. Business Administration Change management
23-01-2017
Supervisor: M.L. Hage Co-assessor: dr. I. Maris-de Bresser
ABSTRACT
During the post-adoptive phase people often revise how they use Information Systems (IS). This paper investigates how and why users revise their system use at feature-level. The goal of this study is to complement to the study of Sun (2012) on Adaptive System Use (ASU) by contextualizing internal and external factors that stimulate users to revise their system use. A theory development research has been done in a Dutch department of an international technology-consulting firm. The findings suggest that individuals are motivated to adapt their system use when they encounter certain specific triggers, like obtaining new information and experiencing overloads, external threats and system limitations. Also, this study found that the way individuals revise their system use depends on the type of user. A distinction can be made between ‘What’s new’ users and ‘Don’t bother me’ users. Therefore both internal motivations as external triggers are suggested to be important contextual factors influencing the way individuals perform ASU.
TABLE OF CONTENTS 1. INTRODUCTION 4 2. THEORY 6 2.1NATURE OF ASU 6 2.2ASU BEHAVIORS 6 2.3TRIGGERS OF ASU 8 3. METHODOLOGY 9 3.1RESEARCH METHOD 9 3.2RESEARCH SETTING 10 3.3DATA COLLECTION 11 3.4DATA ANALYSIS 11 3.5DATA QUALITY 12 VALIDITY 12 RELIABILITY 13 4. RESULTS 14 4.1EXTERNAL TRIGGERS 14 4.2INTERNAL MOTIVATION 17
5. DISCUSSION AND CONCLUSION 20
5.1FINDINGS 20
5.2THEORETICAL IMPLICATIONS 23
5.3MANAGERIAL IMPLICATIONS 23
5.4LIMITATIONS AND FURTHER RESEARCH 23
5.5CONCLUSION 24
6. REFERENCES 25
7. APPENDIX 28
APPENDIXI:DEFINITIONS ASU 28
APPENDIX II:ASU TRIGGERS 29
APPENDIXIII:LIST OF INTERVIEWEES 29
APPENDIXIV:CODEBOOK 30
1. INTRODUCTION
There is a constant pressure on companies to use Information Technology (IT) more effective, efficiently or innovative to be a step ahead of their competitors, especially in the current highly competitive economy. Companies are expected to spend up to $4,5 trillion on IT in 2017 (Gartner Inc, 2013). Even though evidence shows that these IT investments have a positive impact on organizational performance (Rai and Tang, 2014), prior research shows that often IT is not used to its fullest (Ross and Weill, 2002; Jasperson, Carter & Zmud, 2005). IT is meant to be a tool to support business processes, information flows, business analytics and reporting (Seddon, Calvert and Yang 2010) but when it does not live up to the expectations of the user, or it fails to help an individual accomplish his/her task, one may be stimulated to look for new options (Sun, 2012). For example, imagine the scenario that a Customer Relationship Management (CRM) system only gives the possibility to show last month’s reports. When one wants to gather all reports on a yearly basis, one may be stimulated to substitute features from another system, like Excel, to still be able to accomplish the task. Individuals revise their Features-In-Use (FIU) to meet existing but unmet needs and apply them to new job demands (Saga and Zmud, 1993). This flux in a user’s FIU is called Adaptive System Use (ASU), which is defined as a users revision regarding what and how features are used in the post-adoptive stage (Sun, 2012). Post-adoptive system use is often characterized by cycles of adaptation, which enables users to exploit and extend the potential of an information system and contributes to enhancing task performance (Jasperson et al., 2005).
One reason to study this question is because there is a gap in the literature on how and why individuals differ in their specific adaptive system use (Sun, 2012; Bala and Venkatesh, 2016). Bala and Venkatesh (2016) state that there has been a limited understanding of specific adaptation behaviors that individuals undertake to cope with an IT, and the triggers of these behaviors. Another reason for investigating this concept is that it is difficult for managers to identify what affects employees’ willingness to engage in innovative behaviors with technologies (Ahuja & Thatcher, 2005). However, when there is more knowledge about different ASU behaviors and its triggers, measures can be taken to constrain or encourage this behavior, through which managers can exploit and extend the potential of an Information System (IS) (Jasperson et al, 2005). If more attention is given to issues related to contextual factors that influence users to adapt their system use, it will help organizations to better manage IT and therefore optimize its use. Therefore, the contribution of this study is twofold. From a research perspective, the theoretical argument is that the literature on ASU still lacks a view of how and why people revise their FIU. From a practice perspective, until we understand ASU better, achieving benefits from systems is likely to be difficult and unpredictable because managers do not fully understand what stimulates users to adapt their system use. This study will therefore investigate different ASU behaviors at individual level, in order to explain why specific adaptation behavior emerged and thereby get a richer understanding of its triggers.
2. THEORY 2.1 Nature of ASU
It frequently occurs that a system does not fully live up to its expectations, or that it fails to help an individual accomplish his/her tasks. Sun (2012) developed the concept ASU for this kind of post-adoptive system use. ASU refers to how people actively revise their use of system features (Sun, 2012). Sun (2012) states that users subjectively revise how they use IT, which indicates that the human factor plays a big role in how the IT is used. Jasperson et al. (2005) confirm this idea by stating that users often evolve different patterns of feature use over time. This variance in IT use results in the extraction of divergent value from an IT application (Jasperson et al., 2005; DeSanctis and Poole, 1994). Therefore it is important for companies to understand more about the human factor of ASU. To better be able to explain ASU, first the nature of the theory will be described. ASU is based on the Adaptive Structuration Theory (AST) from DeSanctis and Poole (1994). This model explains the interplay between IT, social structures and human interaction (DeSanctis and Poole, 1994; Giddens, 1984; Jones and Karsten, 2008). The theory focuses on the relationship between technology and the context in which technology is used (Hill, Bartol, Tesluk and Langa, 2009). According to this theory it may be hard to predict IT results, due to the human factor. However, when more is known about what drives the human factor, it may help to manage the continued use of IT. As the AST theory indicates, IT is subjected to its context. Therefore the goal of this study is to contextualize ASU behaviors to get a better understanding of how and why users perform ASU. When more is known about why individuals adapt their system use, managers may be better able to stimulate users to reach optimal IT use. In order to clarify ASU more extensively, the next section will describe every specific ASU behavior in detail.
2.2 ASU behaviors
ASU can be divided in two different dimensions, namely content revision and spirit revision. Content revision is described as a user’s revision regarding what features are included in his/her FIU, and includes trying new features or substituting features (Sun, 2012). Spirit revision is described as a user’s revisions regarding how features in his/her FIU are used, and includes combing features and repurposing features (Sun, 2012). In Appendix
I
the definitions regarding ASU behaviors can be found. To explain the four ASU behaviors, examples will be given based on the CRM-system example used in the introduction. Therefore it is important to understand the goal of a CRM system, which is to ‘unite the potential of relationship marketing strategies and IT to create profitable, long-term relationships with customers and other key stakeholders’ (Payne and Frow, 2005 p. 168).invitation. Then, one may be stimulated to look for new features in the CRM-system, for example a calendar feature. When this feature is added to the FIU of this person, he/she can check the availability in the calendar of the coworkers and schedule the meeting, which will prevent wasting time. Trying new features is a commonly observed adaptation behavior and can be viewed as explorative behavior that improves one’s knowledge and mastery of IT features (Sun, 2012). The more experience a user gains with an information system, the more he or she tends to discover its unique features (Sun, 2012). Therefore, users can continue discovering after the system has been adopted (Jasperson et al. 2005).
Secondly, feature substitution refers to the situation where features are replaced with other features with similar functions (Sun, 2012). For example, imagine the scenario that a CRM-system only gives the possibility to show last month’s reports. When one wants to gather all reports on a yearly basis, one may be stimulated to substitute features from another system, like Excel, to still be able to accomplish the task. Now the user can insert the reports on a monthly basis and gather all the information in one file. In this case, both features have the same goal, namely showing the data of the reports, however the CRM-system fails to show data in the right time period. Therefore a user may be stimulated to substitute this feature by using a feature with a similar function.
Thirdly, feature combining is defined as using features in the FIU together for the first time (Sun, 2012). An example of feature combining would be if the CRM-system lacks the possibility to translate files. When this CRM-system is used in an international company, it may be of use to have a translation feature. When this feature is missing, one may be stimulated to use systems like Google Translate next to the CRM-system. These tweaks, workarounds and add-ons show how people combine what they know about deficient system features to bypass system limitations (Sun, 2012).
Fourthly, feature repurposing is defined as using the features in the FIU in a new way (Sun, 2012). Image that there is a feature named ‘workgroup’, which is intentionally designed to work together with other coworkers for one client. If one often collects interesting files and documents that is of only of interest to him/her but does not know where to keep these files, one may be stimulated to repurpose his/her features. For example, one may decide to use a workgroup as a way to gather this information. The feature was intentionally created to be able to work together in the CRM-system, but is now repurposed as a feature to collect and reserve files and documents. Not all features are revisable. Some are more restrictive than other, for example it would be impossible to repurpose the feature ‘log in’. Therefore only a part of one’s FIU is available for ASU. Table 1 provides an overview of the four described ASU behaviors can be found.
Table 1. Types of ASU (Sun, 2012)
Feature combining Using features in FIU together for the first time Feature repurposing Using features in one’s FIU in a new way
Sun (2012) focused on this collection of specific behaviors for ASU, to create an overarching concept for user adaptation to IT. According to Bala and Venkatesh (2016) the focus often lies on how users can be motivated to use features as intended to. Yet, this may be not the most efficient way of using IT for every individual. Individuals adapt their system use to optimize their use, but the contextual factors under which this behavior emerges are still unclear. Sun (2012) already established three triggers that stimulate people to perform ASU, which offers an invaluable starting ground for contextualizing ASU. In the next section, these triggers will be clarified. From there on, a better understanding of the importance of this study will be expounded.
2.3 Triggers of ASU
from Louis and Sutton (1991) indicate that there are triggers in the environmental conditions that lead people to abandon their habits and switch to active thinking. However, it may also be possible that there is an internal motivation for switching to active thinking and performing innovative behavior, which has not been included in the study from Sun (2012). To conclude, the triggers used by Sun (2012), provide an invaluable starting ground but are at the same time still vague concepts. According to Sun (2012) ASU is contingent upon individual and contextual factors. However, it is unclear under which contextual factors ASU is performed. For this reason, the goal of this study is to complement the research from Sun (2012) by investigating ASU at individual-level to get an idea of how and why users are motivated to perform ASU behaviors. As suggested by previous research ASU is contingent upon individual and contextual factors (Sun, 2012; Louis and Sutton, 1991). The AST theory shows that the human factor influences how individuals use IT. However, the human factor can be subject to different contextual factors. Therefore the figure underneath was created as a basis for this research. The goal of this study is to investigate individual and contextual factors of ASU behaviors.
3. METHODOLOGY 3.1 Research method
behavior and explaining individual’s perceptions, which is in line with the goal of this research. The goal of this research is to develop propositions additional to the constructs of the concept ASU, which can also be referred to as theory development (van Aken, Berends & van der Bij, 2012). Theory development is well suited for explaining undiscovered links among concepts in order to get detailed and rich information (Eisenhardt, 1989). The propositions developed in this study should be able to be tested in the future to see if this is generalizable for other cases.
To make sure that differences between users can be extracted from the data, the focus of this study is on individual-level. Individual ASU behavior will be investigated to explore individual motivations to perform ASU. First, the research question has been established by diving into the literature on post-adoptive system behavior. It was found that individual adaptation behavior is still underexposed. Then a case was selected to investigate individual ASU behaviors in practice. This case was suitable because the company noticed a lot of differences in system use. Next, twenty interviews with the users of this system were done and the data was collected in one full week at the office. To analyze the data, all the interviews were coded and a cross-case analysis was done. This enabled the researcher to understand why users in certain circumstances adapted their system use. Then the results were supplemented by the literature. New insights were transformed into propositions, which are further explained in the discussion section.
3.2 Research setting
the users encountered a huge change during the first year they used the system. Therefore this case has a double level of interest, whereby not only IT behaviors are investigated, but also the effect of a big change.
3.3 Data collection
For this study, semi-structured interviews with open-ended questions were conducted. This enabled structure, while still allowing the interviewee to declare his/her personal situation. All interviews were conducted in Dutch, the native language of the interviewees, to give the interviewees the opportunity to better express themselves. The article from Sun (2012) was used as starting point. However, questions were composed without directly asking about specific ASU behavior or triggers because it was tried to keep the interviews as open as possible. The interviews started with an introduction about the research, after which some general questions were asked about the role of the interviewee, which role the system plays for this interviewee and whether or not this has changed. Next, a large section on specific questions like: ‘how did your system use evolve over time’ and ‘why did your system use change’ were asked to get an idea of why ASU emerged. Furthermore, a mentor at the company helped making the questions more concrete and specific to this case. This mentor was present during the first two interviews and helped specifying some questions. The interviews lasted thirty-fifty minutes. At the beginning of the interview, the interviewee was informed that the interview was fully confidential and names, dates or other information that could be traced back to the interviewee would be removed from the data. Also, the interviews were recorded on tape so no information would be left out. Before the interview started all interviewees agreed that the interview could be audio taped.
3.4 Data analysis
deductive codes (Appendix IV). A cross-case data analysis has been done to compare the information per case. An Excel file was created to put in all the codes per participant, to make it easier to analyze the data per case (Miles and Huberman, 1994). This coding process made it possible to analyze a large amount of data collected for this study. Furthermore, notes were used while analyzing the data, to keep track of important information, which was the first step in composing the propositions. These notes were used to keep track of triggers that motivated individuals to revise their system use. Lastly, to make it easier to get an overview of the most important codes, a categorization framework was made to categorize the codes. Below, the table (table 2) with the categories can be found.
Table 2. Categorization framework
The system System goal, system advantages, system limitations The company Management expectations, facilitating conditions
The user Characteristics, opinion system, opinion change process, motivation to use, expectations, skills, development, demand and priority. System use System use, reason to use, change in use, behavior due to limitations,
using additional systems, asking help. Differences between users User perception
Innovative system use Exploring system, effective use
ASU ASU, Trying new features, feature substituting, combining and repurposing, Trigger ASU
Super user experience Super user experience
3.5 Data quality
To ensure high quality data, criteria like validity and reliability were taken into account (Swanborn 1996; Yin, 1994).
Validity
internal mentor who works at the firm, to make sure that the questions are correct and specific enough to get relevant information. This internal mentor guides master students to conduct more relevant information for their thesis. She added some questions about advanced features to ensure that the questions fit the context. Through this more specific questions could be asked. The risk of changing the questions in the best of interest of the company was taken into account, however, considering her role within the company this risk was considered to be small. Second of all, internal validity is about the extent to which conclusions about relationships are justified and complete. To make sure this study is internally valid, the interviews were audiotaped to make sure no data got lost. Not only users have been interviewed but also support staff. Thereby multiple points of views were taken into account to get a good overview of the situation. Also, the relationships presented by this study are supported by quotes from the interviewees. Furthermore, the relationships found by this study were compared to other literature to see if the conclusions could be confirmed by other theories. Lastly, this study tried to reach data saturation. It was tried to code as correct as possible, by coding the data in two rounds. Also, enough information was obtained to replicate the study (Walker, 2012; Guest, Bunce and Johnson, 2006; Fusch and Ness, 2015).
Reliability
4. RESULTS
In this section the results of this study will be presented. First, the ASU behaviors as explained by the users will be described. From here on, the triggers that motivated the users to adapt their system use will be established. Next, an overview of internal motivation for ASU will be given. Finally, a difstinction will be made between internal motivations and external triggers, which will help to answer why there is such a wide variance in adaptive system use between individuals. To guarantee anonymity, employees are referred to as a code (Appendix III).
4.1 External triggers
The second behavior that came to notice was ‘feature substituting’. The trigger for this kind of behavior was often due to system limitations. When a system has its limitations, the system does not support the user to complete the tasks in the right way, whereby users need to revise their system use. CPM6: ‘it is too hard to insert some specific kind of data, so I still keep a shadow administration in Excel and insert only the general amounts’. Both features from the CRM-system and Excel had the same goal, namely offering an overview of data, but in the CRM-system it was too hard to use certain features. In this case the participant was not able to distract the data from the system and found it more convenient to use Excel. Therefore another feature from another system with the same function was used. This indicates that a failure in using the feature may lead to substituting other features in the FIU. Other users confirm this kind of behavior. CPM7: ‘due to system limitations, we are forced to start with work-arounds’. There was even a user who used a piece of paper to keep on overview of its own clients (CPM 3). However, it may also be possible that the user is not that skilled with IT and therefore is unable to find the feature that had to be used and therefore substitutes it with a feature that is already in his/her FIU. CPF5: ‘I just cannot find the feature that gives me an overview of the amount of money I sold, so I just put it in Excel. However, it would be nice that I would be able to compare it with the real data I inserted in the system’. In this case, the user may feel like he/she does not have the right resources, like IT skills, to accomplish the task, which can be referred to as an overload. So, it may be suggested that feature substituting can be triggered by system limitations, or a perceived overload.
The fourth behavior that came to notice was feature repurposing. CPF18: ‘we often get information from sales that is not that accurate, but I have to insert the right data into another system. Therefore I use the CRM-system to put the data in alphabetic order, to save time. In this way, I don’t have to search for the data’. This user was not obligated to work in the system, but found a way to use the system in a new, unanticipated way that the developers did not foresee. This was triggered by a need for making the tasks more efficient to reduce time and therefore is related to task overload. Apparently there were some limitations from the environment, namely inaccurate data, which stimulated the user to structure the obtained data and to save time. In addition, co-workers with the same work do not use it this way. CPF18: ‘well it is not obligatory. But I had time for it, so I found out with trial and error and now I use it’. This kind of behavior did not occur often because most of employees did not have time for this kind of behavior.
4.2 Internal motivation
system, but also people who embrace the system and optimally use it’. We suggest that this internal motivation to use the system also influences how individuals adapt their system use. This study therefore makes a distinction between ‘don’t bother me’ and ‘what’s new’ users, which is explained in table 3 below.
Table 3. Internal motivation
Code User System use
Goal: don’t bother me
CPM2 I like to find the most efficient way to use the system
‘I have to deliver an overview, and this feature is probably in the system. However, I think it is too much work to find out. Therefore I use my own tool’. CPF5 The things I use the system for
work fine. But I certainly don’t use all the features.
‘It gives me the least administrative pressure to do it likes this. How I can save time for my administration’.
CPM6 I have no idea how to report, but I don’t bother to find out.
‘I tell myself to explore the system, but I just don’t do it. I do not take the time for it.
Code User System use
Goal: use it more extensively
CPM20 I am a techfreak, so I love it! ‘I like to play with the system’. CPM7 I am an early adopter. I like
innovation.
‘I think by myself: ok what are the functions, what do I think is a useful feature and then I give it a try. But I am also an advocate of innovation’.
use the system and therefore also how they adapt their system use. On overview of the characteristics of the two types will be given in table 4 below.
Table 4. Type of user
Don’t bother me - Don’t bother to use and explore system - Only use the obligatory features
- Don’t want to spend time to explore the system
- Spend as little time in system as possible to perform tasks - Effective system use
- System does not offer added value - Don’t need the system to perform tasks
What’s new? - Use more features than the ones that are obligatory - Like to play with the system
- Will give new functions a try - See the added value
- Consider themselves as skilled with IT
To conclude, this study suggests that there are two types of users, namely ‘what’s new’ users and ‘don’t bother me’ users, who have a different internal motivation to use the system, which influences how they adapt their system use. This should be taken into account when managing continued use of IT. When individuals face certain external triggers they may be motivated to revise their system use. However, some external triggers may result in huge threats, which causes abandonment of features even when users were previously motivated to explore the system. Figure 3 shows a summary of the results above.
5. DISCUSSION AND CONCLUSION 5.1 Findings
The aim of this study was to answer the research question: how and why do individuals perform ASU behaviors. The results show that individuals are both stimulated by their own internal motivation to use a system, but also when encountering external triggers. This study complements to the literature by providing an overview ASU behavior stimulated by specific external triggers and by adding the internal motivation to use a system as an important indicator for ASU behavior.
menace the status quo. Then one will try to make sense of the situation. According to Beaudry and Pinsonneault (2005) users cope with an external threat by first estimating what the consequence is and then look how much control they have over the situation. In the case that the external threat has a negative consequence and there is no control over it, it may lead to abandonment of features. Users may feel that the best cause of action is to avoid using the IT to prevent harmful consequences (Bala and Venkatesh, 2016).
Proposition 1: Users are triggered to abandon features when faced with a novel situation like an external threat or system limitations.
Secondly, this study establishes a more specific overview of triggers for ASU. Triggers are embedded in contradictions or interruptions (Sun, 2012). Sun (2012) stated that novel situations and discrepancies were the main triggers for users to perform ASU. These triggers stimulate people to switch from an automatic mode of thinking to a more active mode of thinking (Louis & Sutton, 1991) and do not necessarily trigger ASU. Rather than the general triggers Sun (2012) established to perform ASU, this study contextualized these triggers. First, it can be suggested that users are motivated to try new features when they obtain new information, or when there is a perceived overload. This can be linked to the trigger ‘novel situation’ trigger from Sun (2012). Secondly, as stated above, an external threat or system limitations may trigger abandonment of the features. This can be linked to the ‘discrepancies’ trigger from Sun (2012), whereby the outcome of using the system is different from what was expected. System limitations may also lead to feature substituting or combining, because the user still needs to accomplish its task, and thus needs the functionalities. Boudreau and Robey (2005) state that users can compensate for system limitations by using tweaks, work-arounds and add-ons to supplement the features that they are using. Thirdly, this study suggests that repurposing features may be the effect of an overload, because then they are motivated to look for novel situation to solve this overload.
Proposition 2: Users are triggered to try new features, when faced with novel situations like obtaining new information or an overload.
Proposition 3: Users are triggered to substitute or combine features, when faced with discrepancies like system limitations.
Proposition 4: Users are triggered to repurpose features, when faced with a novel situation like an overload.
we call ‘don’t bother me’ users. They don’t want to explore the system and therefore make their system use as efficient as possible. Bala and Venkatesh (2016) confirm this idea but stating that users with low involvement will have no inherent motivation to learn about the potential positive consequences of IT. The reason why those users don’t want to explore the system is because it does not add value for them. This may be due to their job description, but also their personality may a role. This type of user will focus on getting a more efficient system use and therefore not perform types of ASU like finding new features. On the other hand there are people who like to explore the system. Those types of users we call ‘what’s new’ users. As indicated by Bala and Venkatesh (2016) there are people with high involvement who are highly motivated to learn about IT. Those users will give new functions a try to see whether or not it adds value for them. One reason why those users explore the system is because they see the value the system can add and because they consider themselves as skilled with IT. Users who are innovative with IT are supposedly more motivated to explore the system than users who don’t (Agarwal and Karahanna, 2000). They are likely to perform all kinds of ASU, but most of all they are likely to try new features.
Proposition 5: The internal motivation to use a system has influence on how people adapt their system use, whereby a difference can be made between ‘don’t bother me’ and ‘what’s new’ types.
Lastly, this study suggests that the internal motivation to use a system may change because of external triggers. The results show that ‘what’s new’ types can be demotivated to explore features because of an external threat. According to Liang and Xue (2009) users will try to avoid the IT to minimize negative consequences of a threat, and restore emotional stability. Therefore the ‘what’s new’ type can change into a ‘don’t bother me’ type and abandon some features because of anxiety. On the other hand, for ‘don’t bother me’ types it is important that they see the value a system can add. According to Bala and Venkatesh (2016) a user is likely to feel the urge to learn, explore, and master it if an IT is perceived to be important and relevant by an employee. An example of accomplishing this is by offering training. However, the data of this study does not show the influence of perceived added value on the internal motivation of ‘don’t bother me’ types. Therefore this would be a very interesting case to study in the future.
Proposition 6: ‘What’s new’ users can be motivated to abandon features by experiencing an external threat.
However, people may change their internal motivation to use an IT when they encounter certain IT events. So, ASU behavior is subjected to both external triggers and internal motivation of the users. Even though these results provide a starting point for understanding the dynamics of ASU, a lot is still unknown about the influence of external triggers and internal motivation and the interaction between those.
5.2 Theoretical implications
This study contributes to the literature streams of adaptive system use and its triggers, by giving an insight in the importance of individual and contextual factors. Except for the 4 kinds of ASU, this study adds another kind of behavior, namely feature abandonment. Also, a more specific overview of ASU triggers has been established, whereby triggers have been linked to specific ASU behavior. Lastly, the most important contribution made is that the internal motivation may have a big influence on how individuals revise their system use. We suggest that the internal motivation to use IT may be changed by certain external triggers. However, this should be tested in future research. There is no doubt that contextual factors can play critical roles in the outcomes of IT use, but the difficulty is that there are no clear-cut patterns that indicate how they influence each other.
5.3 Managerial implications
The most important managerial implication is that the internal motivation of users can influence how and why individuals use IT, and therefore how they revise their system use. Thereby a distinction can be made between explorative types and types who don’t bother to use the system. Companies should be aware of the distinction between different user types in managing the use of IT. Important to know, is that explorative users may be demotivated by some events to stop exploring the system and abandon features. On the other hand, users can be stimulated to revise their FIU when they encounter certain external triggers. This may result in trying new features, but also in abandoning features. By keeping that in mind, managers can influence users on many levels when managing continued use of IT. When stimulated wrongly, there is a chance that users will abandon its FIU. But when stimulated in the right way, users may explore the system more extensively, and thereby improve the fit between task and IT.
5.4 Limitations and further research
the last year. Therefore observations during ASU episodes are necessary to get a more rich understanding of all the contextual factors influencing ASU. Lastly, during the first year an external threat occurred, whereby people felt threatened to use the system. This threat was very insightful for this study, because now a change in behavior due to a change in environmental context could be investigated. However, it also would be interesting to see how external opportunities would influence ASU, to get a richer understanding of the influence of contextual threats and opportunities from the environment.
For further research, it would be interesting to investigate how the internal motivation of individuals influence the way they adapt their system use. There is a possibility that there are additional internal motivations to use a system. Therefore we would recommend investigating different internal motivations to use a system and how this influences ASU. A further investigation on the human factor is necessary to fully understand ASU. Furthermore this study comes up with external triggers that lead to specific behavior, but triggers do not always guarantee specific behavior. It would be interesting to explore if there are additional external triggers that motivate individuals to adapt their system use, and if so to test if these specific triggers also lead to specific ASU behaviors in other cases. In sum, this study provides a good starting point that shows the importance of individual and contextual factors, but for further research we would recommend to get a deeper insight in how these factors influence ASU.
5.5 Conclusion
The following research question will be answered in this section: how and why do individuals perform specific ASU behaviors?
contextualizing these triggers. This study does highlight that when a company wants to use its IT optimally, both internal motivation and external triggers have to be taken into account. In addition, we found that external triggers can change the internal motivation of individuals. One may be very motivated to explore the system, but when faced to an external threat, one may get demotivated and abandon the features in their FIU. This is not the most promising result for companies, but this study hopes to remind managers of the important factors that have to be taken into account when managing IT use. IT investments will rise yearly and when organizations tend to use IT optimally it is important to keep in mind that individuals are influenced by a lot of factors when revising their FIU.
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7. APPENDIX APPENDIX I: Definitions ASU
Dimensions and sub-dimensions of Adaptive System Use (Sun, 2012. Page 456).
Adaptive System Use: User’s revisions regarding what and how features are used to exploit and extend the potential of a system (Sun, 2012)
Construct Definition
Feature-In-Use (FIU) The basket of system features that are already used by a particular user to accomplish tasks
Revision of the content of FIU: A user’s revisions regarding what features are included in his/her FIU: what features are used.
Trying new features Add new features to one’s FIU and thus expanding the scope of the FIU.
Feature substituting Replacing features in the FIU with other features with similar functions.
Revision of the spirit of FIU: A user’s revisions regarding how features in his/her FIU are used. Feature combining Using features in FIU together for the first time
APPENDIX II: ASU triggers ASU triggers (Sun, 2012 p. 459)
Novel situations Situations where a person encounters things that are unfamiliar, previously unknown, unique, or that appear to be out of ordinary
Discrepancies Situations where an unexpected failure, a disruption, or a significant difference exists between expectations and the reality
Deliberate initiatives Initiatives one takes in response to a request for an increased level of attention, when asked to think, or while being explicitly questioned.
APPENDIX III: List of interviewees List of interviewees
Code Function Time (in minutes)
CPF1 Account manager 82:29
CPM2 Account manager 38:25
CPM3 Account manager 27:57
CPM4 Sales controller (super user) 51:33 CPF5 Account manager + expertise manager 22:22 CPM6 Account manager 17:13 CPM7 Account manager 53:11 CPM8 Account manager (expertise manager) 23:57 CPM9 Operations manager + sales support 39:23 CPF10 Secretary 18:04 CPF11 Dealmaker (business development) 23:03 CPF12 CRM consultant (sales controller & data quality)
80:34
CPF13 Account manager (expertise manager)
CPM14 Manager sales operations & bid management
29:18
CPM15 Account manager 26:48
CPM16 Super user 51:32
CPM17 Sales support 18:38
CPF18 Proposal officer manager 16:28 CPF19 Sales operations (super
user)
46:34
CPM20 System change
ambassador for marketing
34:58
APPENDIX IV: Codebook
Legend codebook Inductive codes Deductive codes
Codebook
System
Code Operational description Quote
System goal The goal of the system, as perceived by its users.
CPM3: ‘In this system I record my customer data’. System advantages The advantages of the system CPF18: ‘this system is
clearer, easier to work with’. System limitations The limitations of the system CPF1: ‘this function is not
supported by the system’. Company
Code Operational description Quote
Management expectations The expectations of the management regarding the system and its use
CPM9: ‘you are obligated to put your opportunity’s in the system’.
Facilitating conditions The facilitating conditions of the company, for example offering support and time for system users.
User
Code Operational description Quote
Characteristics users Characteristics of the user, like personality or behavior
CPM7: ‘I am an early adopter of the system’. Opinion about system Opinion from the user about the
system
CPF1: ‘For me it is necessary evil’. Opinion change process
system
The opinion of the users about the changes that were made regarding the system
CPF1: ‘After the
implementation some things changed, which was not communicated very well’. Motivation to use The motivation of the user to use the
system
CPM2: ‘Because these features are the only one’s I cannot avoid’.
Expectations of user Expectations of the user regarding the system
CPM3: ‘I think the
administrative task could be way easier’.
Skills user Skills of the user regarding the system
CPM3: ‘If I would have to grade myself, it would be a 6’.
Development user The way the user developed itself CPF5: ‘In the beginning I made some mistakes, but now I know exactly what to do’. User demand The demands of the user regarding
the system
CPM20: ‘It would be nice to have a guidance’.
Priority user The priority of the user CPF5: ‘This is not the priority, you are very busy and if you want to explore you need to do it at home’. System use
Code Operational description Quote
System use The way the system is used CPF1: ‘I don’t use the system very actively’.
Reason (not) to use system The reason the system is used CPF13: ‘I stopped using it, because of the rumors about our competitor’.
the system bonus when all your bookings are in the system. So you have to’.
Change in system use The change in system use CPF5: ‘Now everything goes a lot faster’.
Behavior due to system limitations
Behavior due to system limitations CPF5: ‘I have a problem with my dashboard, so therefore I use Excel now’. Using additional systems The use of additional systems CPF1: ‘I rather use my own phone for customer contact’. Asking help The process of asking help from the
users of the system
CPM17: ‘When I have questions most of the time I ask colleagues’.
Differences between users
Code Operational description Quote
User perceptions about differences in system use
The user perceptions about differences in system use per individual
CPM20: ‘one account is passionately using the system, while others are almost not working with it’. Innovative system use
Code Operational description Quote
Trying to explore the system Trying to explore the system, how and why?
CPF5: ‘Now I am interested in what I can get more out of it’.
Effective system use Effective system use from the users CPF5: ‘Because for me, this give me the least
administrative pressure’. Adaptive system use
Code Operational description Quote
Adaptive system use Adaptive system use from the users CPM2: ‘And then I try to find the most effective way’. Trying new features Add new features + expand scope CPM7: ‘I was used to send
Feature substituting Replace similar features CPF5: ‘I can not see the data in one click, so I use Excel’. Feature combining User features together CPF5: ‘I use Excel to fill in
data into the system rightfully’.
Feature repurposing Use features in a new way CPF18: ‘we often get
information from sales that is not that accurate, but I have to insert the right data into another system. Therefore I use the CRM-system to put the data in alphabetic order, to save time’.
Trigger ASU Things that trigger ASU CPM7: ‘My colleague
showed me the functionalities of a dashboard, so I want to ask for help to get the same’. Super user experience
Code Operational description Quote
Super user experience The experience the super user has in regard to the system and its users
CPF12: ‘Sometimes you need to obligate people’.
APPENDIX V: Interview questions Interview questions
1. General information
Ø What is your role in the company?
o Which responsibilities do you have? o What are your tasks?
2. The system
Ø What is the role of the system when accomplishing your tasks? Ø What do you think about the system?
Ø How skilled are you with the system?
o And in comparison with your colleagues? 3. Triggers
o How did you solve this?
Ø What has been communicated about the management expectations? 4. System use
Ø What features do you know? Ø Which of those features do you use? Ø How did your use evolve over time? Ø What advanced features do you use?
o Do you use the dashboards? Why? o Why do you use the feature ‘chatter’? o Do you also use the application?
Ø What do you think about the entering of your competitor to the system? o What does this mean for your system use?
Ø What other system do you use?
o How do these systems complement to your CRM-system? Ø How is your use in comparison to your colleagues?
o What caused the difference? 5. Wrap up
Ø What did you think about the interview? Ø Do you want to add anything?
Ø Do you want to receive a transcript?
