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TRIGGERS AND OUTCOMES OF
ADAPTIVE SYSTEM USE
AN EXPLORATIVE STUDY ON THE TRIGGERS AND OUTCOMES
OF ADAPTIVE SYSTEM USE WITHIN SOFTWARE
DEVELOPMENT TEAMS
Master Thesis, MSc Business Administration Change Management University of Groningen, Faculty of Economics and Business
July, 2016 C. R. Schut - S2800926 Het ruiterpad 9 8252 GV Dronten M: +31 (0)6 13 89 78 81 E: chantalschut@gmail.com Supervisor: dr. B. Müller Co-assessor: dr. I. Maris-de Bresser
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Abstract
Organizations invest heavily in IT systems, but there is evidence that these systems are underutilized by the users. Users can revise their system use and thereby further exploit or explore an IT system. This revision is named Adaptive System Use (ASU). So far, little is known about the triggers and outcomes of ASU in a complex technological context. This paper answers the research question of what are the triggers and outcomes of ASU in software development teams. Software development teams operate in a complex context, need a high level of collaboration and need to consider requests from multiple stakeholders which influences the type of triggers and outcomes of ASU. In total 16 semi-structured interviews were conducted among four software development teams. The results indicated positive organizational outcomes from ASU such as improved attitude, performance and efficiency. Since the outcomes of ASU are positive, this type of behaviour could be stimulated. The following triggers of ASU were found: novel situations, team context, leadership, personal characteristics, knowledge and cognitive appraisal. All of these triggers and outcomes also have sub-triggers and sub-outcomes.
Acknowledgements
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Table of content
1. Introduction ... 4
2. Theoretical Background ... 7
2.1. Adaptive System Use (ASU) ... 7
2.2. Possible triggers for ASU ... 8
2.3. Possible outcomes of ASU ... 14
3. Methodology ... 16 3.1. Research approach ... 16 3.2. Research setting ... 16 3.3. Data collection ... 17 3.4. Data analysis ... 18 4. Results ... 20
4.1. Triggers of Adaptive System Use ... 20
4.2. Outcomes of Adaptive System Use ... 25
5. Discussion ... 27
5.1. Summary of the findings ... 27
5.1.1. Triggers for ASU ... 28
5.1.2. Outcomes of ASU ... 31
6. Conclusion ... 33
6.1. Theoretical implications ... 33
6.2. Practical implications ... 33
6.3. Limitations and future research ... 34
6.4. Conclusion ... 35
References ... 36
Appendixes ... 40
Appendix A. Interview guide for the managers ... 40
Appendix B. Interview employees ... 42
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1. Introduction
Organizations rely on Information Technology (IT) to improve their competitiveness and to survive in the market (Wang, Li, and Hsieh, 2013). Even though IT is integrated in many innovations and it is likely that IT benefits innovation, the role of how IT helps companies to become and stay innovative remains unclear (Gordon and Tarafdar, 2007). Organizations have invested heavily in IT (Jasperson, Carter, and Zmud, 2005). In 2008 this was around 50% of their capital in IT, which is about $ 3.4 trillion of the budget (Wang et al., 2013). Organizations are dependent on IT as the main support of their business with clients (Jasperson et al. 2005). However, organizations are generally not pleased with the performance of IT projects (Thite, 2000) and there is evidence that organizations do not use the full potential of current IT systems which means that features are underutilized (Jasperson et al., 2005; Bagayogo, Bassellier and Lapointe, 2014; Veiga, Keupp, Floyd and Kellermanns, 2014; De Waal and Knott, 2013). This underutilization of features of IT systems is a large problem for organizations. Therefore it is necessary for organizations to understand and to further trigger people to extend their usage, which may lead to more value from present IT systems and needing less capital (Wang et al., 2013).
This extension or revision of using current features of IT is named Adaptive System Use (ASU). ASU could be described as a collection of adaptation behaviours which an individual performs when revising their features in use in a post-adoptive situation (Sun, 2012). To be able to cope with IT events and their consequences, users respond differently and need different adaptation efforts (Folkman, Lazarus, Gruen, and DeLongis, 1986; Bala and Venkatesh, 2016; Beaudry and Pinsonneault, 2005). These adaptation efforts could be divided into exploration or exploitation behaviour of an individual when using a system (Sun, 2012; Bala and Venkatesh, 2016). This exploration of a user trying to find, extend or change features could lead to innovative ways of accomplishing a task (Bala and Venkatesh, 2016). ASU is an innovative type of behaviour (Sun, 2012) and may limit the underutilization of IT systems.
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Previous research focussed on behavioural outcomes (Sun, 2012). However, a relationship between IT usage and performance of this user seems plausible, it may well be that technology adaptation influences their performance (Nevo, Nevo, and Pinsonneault, 2016). However, people could also use features in non-productive ways (Jasperson et al., 2005). Thus, the way a person uses a system may affect their performance (Nevo et al., 2016). Sun (2012) has identified outcomes as a type of behaviour, whereas, Bala and Venkatesh (2016) have compared the effects of technology adaptation behaviours on job performance and job satisfaction. However, there are possibly more outcomes such as job engagement, job anxiety, security etc. (Bala and Venkatesh, 2016). ASU could also lead to higher returns on investment due to utilization of a system (Jasperson et al., 2005).
Adaptation behaviour is rather context-specific than generic (De Waal and Knott, 2013), it is therefore needed to research adaptation efforts in a different technological context (Sun, 2012; Bala and Venkatesh, 2016; Folkman et al., 1986). Researching adaptation behaviour in a team context is needed to be able to compare users of similar IT systems (Wang et al., 2013; Beaudry and Pinsonneault, 2005). Also what is missing in the literature on ASU is information about the respondents (Sun, 2012). Software development teams are characterized by diffuse expertise and a high level of collaboration (Faraj and Sambamurthy, 2006). This could indicate a need for different triggers then independent and unknown respondents. Moreover, previous research focussed on more mature technologies like MS Office (Sun, 2012). However, developing software is a daunting complex socio-technical activity (Sawyer, 2004). These users need to interact with the technology as well as with each other (Sawyer, 2004). This change in complexity and technology may change the effort expectancy of a user or show new triggers. In addition, a software development team has a lot of knowledge about IT systems. When users have a strong belief in controlling their usage, they might start adapting sooner (Beaudry and Pinsonneault, 2005). Therefore, selecting software development teams contributes to the understanding on the adaptation process which could lead to different usage behaviours than people without affinity with IT.
The research on ASU is very limited and needs to be extended with another more complex technological context. Software development teams need to communicate a lot due to complex project(s) and therefore may have different triggers and outcomes than a mature system like MS Office. Moreover, the outcomes of ASU are unknown in the literature field. The goal of this research is to understand how ASU behaviour can be triggered and what the outcomes of ASU are within software development teams. This leads to the following research question:
What are the triggers and outcomes of Adaptive System Use behaviours in software development teams?
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help managers decide whether to encourage or constrain ASU behaviour (Sun, 2012). The outcomes could be positive or negative but only when positive ASU could be enhanced to achieve a higher level of usage an gain more value from installed IT systems which is a worthy effort and much less need for financial investment (Wang et al., 2013). Establishing the triggers for ASU helps organizations in forming aggressive strategies to stimulate users to widen their usage of available IT systems (Jasperson et al., 2005). Thus, managers should give attention to innovative usage of IT, such as ASU, during the post-adoption stage (Wang et al., 2013).
Sun (2012) focusses on the implementation phase. However, it is important to consider the post-adoption phase because an individual needs to be familiar with a system before one could exploit or explore a system (Wang et al., 2013; Jasperson et al. 2005). Moreover, innovating with IT is likely to happen during the post-adoption phase because users have passed the initial usage and gain more experience with the system (Wang et al., 2013). This research will contribute to the literature field by focussing on actual triggers for adaptation behaviours. This will lead to richer research model in studying the variation in technology adaptation behaviours across post-adoptive behaviours (Jasperson et al. 2005). Understanding these triggers of ASU is of great importance because it is a way to maximise the returns on IT investments (Wang et al., 2013). Moreover, this research will contribute by exploring the outcomes from the ASU behaviours.
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2. Theoretical Background
First the concept Adaptive System Use (ASU) is explained. Thereafter, the possible triggers and outcomes of ASU are described.
2.1. Adaptive System Use (ASU)
ASU is a collection of adaptation behaviours which describes how people revise their system features in use after IT system implementation (Sun, 2012). This revision allows for an exploitation or exploration of the system usage by individuals (Bala and Venkatesh, 2016; Sun, 2012). Exploitation refers to a routine of using and utilising an advised or taught set of features (Bala and Venkatesh, 2016). Exploration is defined as an active and independent search for information and/or innovative ways of doing things which exceed the requirements of an organization (Bala and Venkatesh, 2016; Gupta, Smith, and Shalley, 2006; Barki, Titah, and Boffo, 2007; Sun, 2012).
Before exploring or exploiting a system users have certain Features In Use (FIU). These are the features known and used by a user to accomplish a task (Sun, 2012). FIU is divided in revising the content and revising the spirit of FIU (Sun, 2012). The content refers to what features are used and incorporates behaviours such as trying new features and feature substituting. The spirit of FIU refers to how the features are used and incorporates behaviours such as combining features and repurposing features (Sun, 2012). An overview can be found in Figure 1. By revising the content other options in terms of features are explored which prevents underutilization. In addition, revising the spirit of FIU refers to innovating with IT and thus extending the possibilities of a system. Thus, in total ASU could lead to innovative behaviour and could prevent underutilization.
Figure 1. Adaptive System use (Sun, 2012, p. 458)
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similar to constructs such as independent exploration behaviours which refer to searching independently to gain knowledge and expertise of an IT and go above and beyond expectations of a company (Barki et al., 2007). An example of trying new features could be when one is familiar with working with Microsoft Office (MS) Word and is able to accomplish their task of word processing. One may try new features, used trial and error or played around with features, which could lead to a feature such as Comments. This feature may not have been in one’s FIU but could now be added. Feature substituting is an active explorative search for a possibility to replace features with other equivalent features (Sun, 2012). This could be occurring due to the absence of features or not relying on these any more. MS Word is used again as an example. Substituting could occur when a person relies on comments to enlist the changes made in a document and substitutes this for another feature like Track & Trace.
Feature combining is about using features together to create new functionality (Sun, 2012). These combinations may be used to innovate with the system or create work-arounds in order to deal with the limitations of a system (Sun, 2012). An example of feature combining may be to combine Track & Trace and Comments in Word. When a user would combine these he/she would be able to change a sentence (Track & Trace) and explain the change by combining this feature with another feature such as Comments. A person would be able to do (or explain) more by combining the features then when this person would only use one of the features mentioned.
The repurposing feature refers to using features in a novel and innovative way and may go beyond the intentions of the system designers (Sun, 2012; Jasperson et al., 2005; Ahuja and Thatcher, 2005). A person could find a creative workaround to bypass a system restrictiveness (Sun, 2012). The feature of Track & Trace of MS Word could be used with a different purpose. With this feature different people/users are indicated by different colours. A single person may add a user name and a date to a document and thereby change the colours of the Track & Trace feature. The user is able repurpose the function of colours to indicate different users, but then used by a single person. However, sometimes a feature cannot be repurposed, for example the save file button included in the MS Word.
2.2. Possible triggers for ASU
9 Figure 2. Research model of Sun (2012, p. 460)
Venkatesh, 2016; Sun, 2012; Bruque, Moyano, and Eisenber, 2008) could be of influence on adaptation behaviours. Moreover, deliberate initiative is the direct requirement to do something which could come from a leader. Other researchers have indicated that leaders should manage discrepancies (Wang et al., 2013; Armstrong and Hardgrave, 2007). In addition, other researchers have also indicated leadership as a possible antecedent for adaptation behaviour (Beaudry and Pinsonneault,
2005; Bruque et al., 2008). Therefore, a new category is made and both triggers are relocated to this category. Facilitating conditions directs towards an infrastructure which could be seen as organizational support. Personal Innovativeness in IT is a personality trait of an individual and other researchers have suggested PIIT as a direct trigger for adaptation behaviour (Wang et al., 2013; Beaudry and Pinsonneault, 2005). Another suggested trigger is self-efficacy (Beaudry and Pinsonneault, 2005; Bruque et al., 2008), which is also considered as a personality trait. Therefore, PIIT is relocated to this new category of personal traits.
2.2.1. Novel situations
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2.2.2. Team context
As mentioned other people use could also refer to observing the usage of other team members. Employees can learn from each other by communication, but also by observing others people system use which triggers ASU (Sun, 2012). Colleagues are the most prevailing source of information within software development teams (Jo, DeLine and Venolia, 2007). Therefore, this new context may lead to different team contextual triggers for ASU. Previous research was conducted among independent respondents (Sun, 2012) and interviewing a team may lead to new triggers like communication, acknowledgement, participative safety and task orientation.
Software development teams have a high level of collaboration (Faraj & Sambamurthy, 2006) and therefore need to communicate often. Communication is also a necessary trigger for innovation (Anderson, Hülsheger, and Salgado, 2009; Gordon and Tarafdar, 2007). It is a technical and organizational competence because besides the face to face communication, communication draws on hardware and software infrastructures (e-mail, skype, groupware etc.) (Gordon and Tarafdar, 2007). Sharing knowledge, experiences and ideas and helping each other will lead to generating new ideas (Van de Ven, 1986). Especially communication with external relations, thus with non-team members or with people outside the organization, leads to creative ideas (Anderson et al., 2009). When team members need to communicate and meet more often, it may lead to more conflicts which lead to critical thinking (Drack-Zahavy and Somech, 2001). In addition, learning from others influences the exploitation behaviour (Bala and Venkatesh, 2016) and therefore could also be relevant for ASU. A team should acknowledge creative ideas and accept unsuccessful attempts to be innovative because that would lead to new attempts to be creative and innovation will be likely (Anderson et al., 2009; Diliello and Houghton, 2006). This also relates to participative safety. Members in a team should be able to participate in the decision making process in a nonthreatening atmosphere including trust and support (Anderson et al., 2009). When ideas are acknowledged, communicated and safe to share it may trigger innovative behaviour like ASU.
Task orientation is a search for excellence (Anderson et al., 2009). It includes a team reflection “upon the team’s objectives, strategies, and procedures, and evaluates each other’s work to improve team effectiveness and outcomes” (Anderson et al., 2009, p. 1131). Thus, when evaluating one’s work it may also include the way a person completes their task and reveal how other people have revised their system usage (ASU). It could trigger ASU.
2.2.3. Leadership
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discrepancies may also be useful because it could trigger technology adaptations and improve the technology on the long term (Majchrzak, Rice, Malhortra, and Ba, 2000).
Sun (2012) also indicates deliberate initiatives as a trigger for ASU. Deliberate initiatives is a request to alter the usage of particular system features (Sun, 2012). A leader could be a deliberate initiative when requesting a user to do something. Deliberate initiatives may also limit the climate for innovation and might discourage adaptation behaviour (Sun, 2012). When a leader is controlling a team instead of guiding them, it could shift the focus of a team away from creative ideas towards the expectations of the leader (Diliello and Houghton, 2006). The direct relation between deliberate initiative and ASU was not supported (Sun, 2012). However, it could be that a leader influences ASU without requesting it. A leader could also choose a collaborative leadership style which support group creativity best (Woodman, Sawyer, and Griffin, 1993).
It is reasonable to expect that social factors such as what leaders think of a technology may influence the appraisals about a technology of a user. Within software development teams, a leader is a crucial factor contributing to the success of these teams (Faraj and Sambamurthy, 2006). Thus, in this context leadership could be a trigger. These teams could need a different type of leadership style since “scientific/technical employees, particularly in the IT industry, possess certain distinguishing personality and occupational related characteristics, such as high need for autonomy, achievement orientation, first loyalty to profession and second to organisation” (Thite, 2000, p. 235). Leaders in a technical context have excellent technical skills but often have no leadership skills (Thite, 2000). However, a leader should be able to recognize these personal traits and act on it.
A leader could invite employees to challenge the current practices which lead to an open environment where employees feel comfortable to share creative ideas (Diliello and Houghton, 2006). The type of acknowledgement executed by a leader influences creativity. When employees would experiment and he/she would be punished in case it fails, it is very unlikely that employees would experiment again (Diliello and Houghton, 2006). Thus, acknowledging or punishing a user for trying a new feature (which is one of the ASU behaviour) could trigger or limit future ASU behaviour.
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2.2.4. Organizational support
As mentioned facilitating conditions could be relocated under organizational support. Facilitating conditions is the belief that a person holds about organizational and/or technological infrastructures (including resources) which exists to support their ASU behaviour (Sun, 2012). It also refers to providing resources which is a kind of support an organization could deliver (Diliello and Houghton, 2006; Sun, 2012). Also, it is unlikely for individuals to innovate or come up with creative ideas without organizational support (Diliello and Houghton, 2006). Thus, facilitating conditions may be a direct trigger for ASU. Resources are needed in the post-implementation stage to further develop knowledge (Staehr et al., 2012). Also users may choose to adapt their technology use because they obtained more knowledge via training or via a colleague on how to use the technology (Olikowski, 2000).
A group may show increased innovative/creative behaviour when they perceive a choice in accomplishing a task (Diliello and Houghton, 2006) and therefore need a supporting structure/culture that allows autonomy. This structure/culture, which allows autonomy or freedom of choice, could lead to ASU behaviours because users are free to choose whether users would like to revise their system usage.
2.2.5. Personal characteristics
Personal Innovativeness in IT (PIIT) and IT self-efficacy (ITSE) are considered personality traits and are two very relevant individual factors of IT use (Wang et al., 2013). IT use is related to ASU because it is about changing one’s current way of feature usage and is characterized by innovation (Sun, 2012). PIIT is “the willingness of an individual to try out any new information technology” (Agarwal and Prasad, 1998, p. 206). An individual is considered to be innovative when he/she adopts the innovation in an early stage (Rogers, 1995). When users have a high level of PIIT, they would challenge themselves and test new ideas for using the IT (Wang et al. 2013). When these individuals would be rewarded for this behaviour they are more willing to experiment with IT and find new ways of using the system (Wang et al., 2013). PIIT could be a direct for ASU in this software development context.
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2.2.6. Cognitive appraisals
Appraisal and adaptation are of constant influence on each other. Therefore, a shift in an appraisal may occur due to adaptation efforts which influences technology, a work process or affects the individual (Beaudry and Pinsonneault, 2005).
A primary appraisal is about evaluating the expected effects of an IT event, which includes the perceived opportunity and perceived threat (Beaudry and Pinsonneault, 2005; Bala and Venkatesh, 2016). Perceived opportunity is when an individual beliefs that the system would benefit him/her, whereas perceived threat is the belief that the system would bring harm to a person (Bala and Venkatesh, 2016). The secondary appraisal entails perceived controllability which is the belief someone has on their ability to control a technology (Beaudry and Pinsonneault, 2005; Bala and Venkatesh, 2016).
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2.3. Possible outcomes of ASU
Whereas Sun (2012) indicates outcomes in terms of behavioural types, ASU could also lead to organizational outcomes such as performance. It is unknown whether ASU leads to positive or negative outcomes. ASU could lead to extending one’s Features In Use. This high level of usage could improve the productivity and therefore enhances the competences of an organization (Wang et al., 2013).
Revising a system allows for extension and exploitation of the potential which an information system may have, which increases the task performance (Jasperson et al., 2005). Moreover, “since most adaptation efforts are oriented toward reaping the benefits associated with the IT event, they are likely to result in performance improvements such as reducing errors, doing the work faster, and increasing revenues” (Beaudry and Pinsonneault, 2005, p. 500). Bala and Venkatesh (2016) have compared pre-implementation and post-pre-implementation outcomes such as job performance and job satisfaction. The adaptation behaviour of Exploration-to-Innovate (which is similar to all four ASU behaviours) lead to an increase in job performance and job satisfaction after an IT implementation (Bala and Venkatesh, 2016). However, the outcomes during the post-adoption phase remains unknown. Moreover, an extension of new Features In Use (FIU) could be used in non-productive ways (Jasperson et al., 2005). One could not say that ASU is always a good way for using an information system under all circumstances (Sun, 2012). Therefore, the outcomes of ASU should be explored for context of software development.
Effectiveness of a team is divided in two dimensions: performance and attitude. The performance dimension includes productivity, customer service (Kirkman and Rosen, 1999; Kuo, 2004), innovation and cost reduction (Kuo, 2004). Attitude refers to job satisfaction, organizational and/or team commitment (Kirkman and Rosen, 1999; Kuo, 2004). Software development teams who show ASU behaviour could influence these outcomes. However, when users ineffectively use a system they will feel vulnerable and frustrated (Burton-Jones and Grange, 2013) which leads to a decrease in job satisfaction.
15 Figure 4. Overview of expected triggers and outcomes of ASU Figure 3. Expected triggers and outcomes of ASU
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3. Methodology
The methodology describes the procedure of this study. This section starts by describing the research approach followed by the research setting, data collection and data analysis.
3.1. Research approach
As mentioned it is important to explore the triggers and outcomes of ASU in a software development context. The level of analysis are software development teams. Therefore this paper uses qualitative data to obtain rich information from the real-world (Yin, 2014; Berends, Van Aken, and Van der Bij, 2012). When conducting case studies, it is more likely to be able to generate new theories (Eisenhardt, 1989). A case study is “a research strategy which focusses on understanding the dynamics present within single settings” (Eisenhardt, 1989, p. 534). In this case is chosen for a single-case embedded design (Yin, 2014). This was chosen because of data control. Within one company two high product innovation teams and two less product innovation teams were selected. This was chosen because if a company want to be innovative then you need to do thing differently and new and different features are needed. ASU is about revising features and it is likely that innovative behaviour like ASU occurs in innovative teams. It is probable that less triggers occur at less innovative teams and these triggers then should be recognized and triggered. A known limitation when conducting case studies is a lower generalizability (Eisenhardt, 1989).
There are three important research-oriented quality criteria, which are controllability, reliability and validity (Berends et al., 2012). By focusing on these quality criteria, the inter-subjectivity agreements on research results can be improved (Berends et al., 2012). In order to safeguard the controllability of this research this chapter describes the research setting, the data collection and how the data was analyzed. This detailed description would enable an independent researcher to replicate it and therefore also ensures reliability (Berends et al., 2012). The detailed data collection description also allows repeatability (Yin, 2014). The validity consists of construct, internal and external validity and is about how the results are created (Berends et al., 2012; Yin, 2014). The validity is described in the data collection section. In the final discussion chapter the internal validity is raised by comparing results with literature and shaping propositions (Eisenhardt, 1989). It also sharpens generalizability (Eisenhardt, 1989).
3.2. Research setting
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used several Microsoft tooling to develop the software. The company has a Microsoft unless policy. This means that Microsoft should be used unless there is absolutely no other option. Software is mostly developed for “internal” clients. The most important and client is the Gas transporter followed by employees from operations, corporate staff and then the IT department. Table 1 provides information on the teams which were interviewed. Internal refers to employees of the company and external means the employees hired from another company to complete one or multiple projects. The author gained access to the company through the network of a professional relationship. This professional gave me the contact details of a manager at this company, who referred me to the head of the ICT business department.
Table 1. Data on teams at 1 April 2016
Teams Average age Average number of years
employed at the company
Number of internal or external employees
Internal External Internal External Internal External
Team 1 42,2 40,8 9,9 4,1 13 9
Team 2 40,4 36,3 8,4 1,7 5 11
Team 3 47,8 42,2 14,2 5 6 12
Team 4 40,3 38 5,1 2,4 6 13
Table 1. Data on teams
3.3. Data collection
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In total six-teen in-depth semi structured interviews were conducted. These conducted interviews were with 12 team members and four team leaders. This provides insight in the perceptions of the leader and the employees on the ASU of their team. A semi-structured interview was chosen: some questions were prepared, but it left room for improvisation (Myers and Newman, 2007). The prepared questions made it able to find consistent and reliable results, and the flexible setting could lead to new insights. Moreover, the interview protocol was evaluated by an expert which increases the construct validity (Berends et al., 2012). The conducted interviews took about 30 to 45 minutes and were conducted in a private conference room. The interviews were conducted confidentially and the names of the interviewees are not linked to quotes. The participants were all given a code to ensure this confidentiality. All participants gave permission to record the interview and to use it as input for this paper.
The interview guide for the team managers can be found in Appendix A and for the team members in Appendix B. The interview guide is based on the ASU behaviours and starts by establishing the occurrence of ASU, followed by questions about the triggers and outcomes of ASU. By making sure that the concept is completely covered it ensures construct validity (Berends et al., 2012). Thus, first open questions about ASU and its triggers were asked, followed by open questions about the outcomes of ASU. Then open questions about the influence of leadership and the team context on ASU were asked. Finally, when some deductive codes would not come up these would be asked with a closed question. A closed question was always followed up with a how or why question. Also critical probing questions were asked to gain reasons behind ASU behaviour or possible advantages or disadvantages of ASU. This study conducted interviews at a single company and had only one point of data. By only conducting interviews it leaves more room for bias (Berends et al., 2012).
3.4. Data analysis
All of the interviews were recorded and transcribed which makes the research more controllable. The recordings limit the research bias due to the ability of replaying the tape. The transcripts ranged from 7 to 16 pages, with an average of 10 pages and can are available on request.
The data was analysed as described by Eisenhardt (1989). In this paper one team is considered one case. First an within-case analyses was done by deductive and inductive coding. Deductive codes were set upfront and emerged from the literature which can be seen in Figure 3. Per case the deductive codes were analysed. Some deductive codes came up during the open questions and some during closed questions. When a deductive code was found, the corresponding quote per individual of this case was written down. Eventually when all cases were analysed sepereately this gave a comprehensive overview.
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external environment. After the deductive analysis per case, the inductive codes were analysed per case and written down with the corresponding quotes. When new (inductive) codes emerged, previous (already coded) cases would be reconsidered for this new code. This allows the researcher to benefit from emerging subjects (Eisenhardt, 1989). Witin case analysis allows initial theory generation (Eisenhardt, 1989).
After conducting the within case studies and making an comprehensive overview with corresponding quotes, a cross-case pattern search was done. All of the inductive and deductive codes were compared to each other to analyse the differences and similarities between cases. This ensures resutls beyond initial impressions (Eisenhardt, 1989).
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4. Results
Interviewees provided many ASU examples, but the specific behaviour of repurposing features was only given once. First, the triggers for ASU are described followed by the outcomes of ASU. The representation of findings is supported by chains of evidence. The inscription of the colours is
presented in Table 2.
4.1. Triggers of Adaptive System Use
4.1.1. Novel situations
Even though it was expected that novel situations would trigger a person to search for new features it only came up during two interviews. One participant mentioned a new task as a novel trigger and another mentioned the change in the systems environment as a trigger for ASU.
Participants have mentioned the external environment, including customer requests as a trigger for ASU. The changes in the environment could also be seen as a novel situation to the teams. As one interviewee puts it: “You have a new client question and then you analyse the most ideal solution and how you can make it with the available software and then you prefer looking at new features which could answer the question in the best way” (EM023). In Table 3 the chain of evidence can be found. Table 3. Chain of evidence: Novel situations
Code Team 1 Team 2 Team 3 Team 4
EM011 EM012 EM013 EM021 EM022 EM023 EM031 EM032 EM033 EM041 EM042 EM043 New task Changes in the system environment External environment
Table 3. Novel situations
4.1.2. Team context
When asking which trigger mostly determines the ASU behaviour of the software development team member, the most frequent answer was, besides how it would make their work easier, the influence of colleagues. Internal communication was a trigger for 10 out of 12 participants. A participant indicated: “Many times other people mention things like: you can use this or you can use this” (EM013), this also happens during daily team meetings. Managers observed conversations about ASU between team members. Another expected trigger was to see how others use the system. This trigger
Colour Meaning
Trigger for ASU Not a trigger for ASU Not an active trigger for ASU Outcome of ASU
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is closely related to internal communication but only two interviewees explicitly mentioned that looking at how other use the system influences their ASU.
Communicating with external relations triggered ASU behaviour. For example one interviewee communicated with a grid operator, who works with the same system, and learned new features. Besides communicating with this client, also external communication with suppliers lead to ASU because “Microsoft arranges regular meetings for users where new features are discussed” (EM022). Team managers support external communication. Even though team 1 and 2 were considered to be more innovative, fewer respondents have mentioned external communication as a trigger compared to team 3 and 4.
Task orientation was only mentioned by a one team member as a trigger. Network was by mentioned as a trigger by one interviewee. It is came up that when external consultants or employees work at the company they have more experience and are able to consult a greater network because they have worked at several clients but also have their own secondment company. In Table 4 the chain of evidence can be found for the team context.
Table 4. Chain of evidence: Team context
Code Team 1 Team 2 Team 3 Team 4
EM011 EM012 EM013 EM021 EM022 EM023 EM031 EM032 EM033 EM041 EM042 EM043 Internal communication External communication Other people’s use Task orientation Network
Table 4. Team context
4.1.3. Leadership
According to the participants, the leader does not have a direct influence on their ASU behaviour. During the interviews with the managers, they were not able to answer specific questions about the ASU of a team. However, when problems occur then they would discuss it with the team and look together for solutions.
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Leaders (or managers) are perceived by participants as open for ideas and provide support for innovation. In this way they influence the ASU of the team because as noted by an interviewee: “By getting excited when you suggest something new and also by giving you time for it. Thus, not by micromanaging everything. For example by not mentioning things like yes but actually you needed to work on your project” (EM011). Also the managers indicated to provide support for innovation. One team manager was a bit reserved about innovation because of the high risks and the need for constant performance.
Moreover, leaders have an influence by distributing resources. Employees recognize that resources should be available for exploring a system. One participant mentioned: “The influence he can exert is budget driven. He places it at our disposal and he gives us a lot of freedom in how we spend it. So, he mentions you have x amount at your disposal and what you do with it is your choice, but you do not get more then this amount” (EM031). One managers indicated that the budget could limit innovation. The chain of evidence can be found in Table 5.
Table 5. Chain of evidence: Leadership
Code Team 1 Team 2 Team 3 Team 4
EM011 EM012 EM013 EM021 EM022 EM023 EM031 EM032 EM033 EM041 EM042 EM043 Leadership Support for innovation Autonomy Available resources Table 3. Leadership
4.1.4. Organizational support
The only organizational trigger which remained was the facilitation conditions which refers to infrastructure and resources. This is closely related to leadership since the managers approved or disapproved innovative ideas and extensions based on the resources available. An interviewee indicated that sometimes you just need to take the time to experiment with the system instead of asking for time. Another interviewee mentioned that there is a major budget for training and education. In Table 7 the chain of evidence can be found.
Table 6. Chain of evidence: Organizational support
Code Team 1 Team 2 Team 3 Team 4
EM011 EM012 EM013 EM021 EM022 EM023 EM031 EM032 EM033 EM041 EM042 EM043 Facilitating
conditions
Figure 6. Organizational support
4.1.5. Personal characteristics
23
indicated that some people have more interest in innovations and spend time on it in their private lives. There does not seem to be a large difference between the teams.
It was expected that IT self-efficacy would also be a trigger for ASU. Three out of the 12 interviewees mentioned this as a trigger. In Table 7 the chain of evidence can be found.
Table 7. Chain of evidence: Personal characteristics
Code Team 1 Team 2 Team 3 Team 4
EM011 EM012 EM013 EM021 EM022 EM023 EM031 EM032 EM033 EM041 EM042 EM043 PIIT
ITSE
Table 7. Personal characteristics
4.1.6. Knowledge
Another trigger which differs per individual is their experience or their desire to search for information online. Experience is indicated as a trigger for ASU because “when someone has a lot of experience and has done many different projects then they take all those experiences with them and they can point more quickly to something what might can be improved and in that way it helps if you have more experience” (EM011). Closely related to experience are discrepancies. One interviewee had the experience that suggestions from others differ from reality and noted: “I have seen a few times that they say yes well you should not do it that way because then it will not work. And when I try it myself then nothing went wrong” (EM013).
The search for information online also influences ASU since “when you are developing software you also look online in for it and on internet to analyse how other people used it and in that way I found out” (EM021). Other employees also looked for suggestions online. No clear difference between the teams could be detected. In Table 8 the chain of evidence can be found.
Table 8. Chain of evidence: Knowledge
Code Team 1 Team 2 Team 3 Team 4
EM011 EM012 EM013 EM021 EM022 EM023 EM031 EM032 EM033 EM041 EM042 EM043 Experience Search for information online Table 8. Knowledge
4.1.7. Cognitive appraisal
24
Other employees felt there were no alternatives and therefore did not actively revise their system usage. This was due to standardization. As one interviewee puts it: “Particular things are not possible and there are some tools which we cannot use here at this company. We are bounded to Microsoft. Therefore, I won’t try other options because there are some things which would work well, but it would be difficult to introduce” (EM011). Other participants felt they got the freedom to try out systems and to suggest new systems.
The satisfaction one holds over a system influences their ASU. Most employees show ASU behaviour when they are dissatisfied about current features. Another employee indicated: “It is also about what you are used to use. This works for me so I keep on using it” (EM032). This employee would not actively try new features because he is satisfied. There does not seem to be a difference between high innovative and less innovative teams. In Table 9 the chain of evidence can be found.
Table 9. Chain of evidence: Cognitive appraisal
Code Highly innovative Less innovative
Team 1 Team 2 Team 3 Team 4
EM011 EM012 EM013 EM021 EM022 EM023 EM031 EM032 EM033 EM041 EM042 EM043 Perceived
opportunity Dissatisfaction
Table 9. Cognitive appraisal
4.1.8. Perceived Usefulness
The participants actively evaluate how trying new, substituting or combining features could benefit them. Most of the time a perceived benefit is an increase in performance. It was mentioned that: “When you work with the integration server you need to do things differently because of the performance. Integration server is not a tool known for the best performance. Then you need to choose another route” (EM022).
Besides performance, other surprising benefits came up. Perceived Usefulness could be fine grained (besides performance) into effectiveness, energy reduction and simplicity. Out of the 12 participants, eight came up with effectiveness as a trigger for ASU. Two interviewees indicated effectiveness as the most determining trigger for ASU. Secondly, energy reduction is considered as an important trigger for ASU because “As a software developer you are lazy by nature. This is actually a good characteristic. Because when you are working on something and you need to do the same action for the second time, well then we could automatize it. Thus, you are actually constantly looking at how we could place everything behind a button or behind a program so we do not need to do it manually” (EM011). Others also try to automatize as many actions as possible.
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Table 10. Chain of evidence: Perceived Usefulness
Code Team 1 Team 2 Team 3 Team 4
EM011 EM012 EM013 EM021 EM022 EM023 EM031 EM032 EM033 EM041 EM042 EM043 Perceived Usefulness Effectiveness Energy reduction Simplicity
Table 10. Perceived Usefulness
4.2. Outcomes of Adaptive System Use
4.2.1. Attitude
The only attitude outcome is job satisfaction. This outcome of ASU is equally distributed over the teams as can be seen in Table 11. As one interviewee mentions: “Yes, because if it would be strict and all routes would be closed and the freedom to see what is out there and it would be very limited, that would not make me happy” (EM013).
Table 11. Chain of evidence: Attitude
Code Team 1 Team 2 Team 3 Team 4
EM011 EM012 EM013 EM021 EM022 EM023 EM031 EM032 EM033 EM041 EM042 EM043 Job
satisfaction
Table 12. Attitude
4.2.2. Performance
The performance outcomes mentioned were quality, overview and continuity. In total seven out of 12 interviewees think quality is an important performance outcome of ASU. Because “when we do something it needs deliver qualitative work. If it does not, there is no point in doing it” (EM021). Creating a better overview of the work for themselves was an outcome of ASU. Another performance outcome mentioned by two interviewees was continuity, because they thought it is important to work in a consistent manner. In Table 12 the chain of evidence can be found for performance.
Table 12. Chain of evidence: Performance
Code Team 1 Team 2 Team 3 Team 4
EM011 EM012 EM013 EM021 EM022 EM023 EM031 EM032 EM033 EM041 EM042 EM043 Quality
Overview Continuity
Table 12. Performance
4.2.3. Efficiency
26
described this as an important outcome. Finally, it should make the work easier. In Table 13 the chain of evidence can be found for efficiency.
Table 13. Chain of evidence: Efficiency
Code Team 1 Team 2 Team 3 Team 4
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5. Discussion
5.1. Summary of the findings
First the conclusions of this study are presented by answering the research question. The next section discusses the theoretical implications followed by the practical implications. Finally, the limitations and future research areas are described.
Research question: What are the triggers and outcomes of Adaptive System Use in software
development teams?
Based on the insights of this study the conceptual model (Figure 4) can be proposed and answers the research question. Results from this study show the following triggers for ASU: novel situations, team context, leadership, personal characteristics, knowledge and cognitive appraisal. Also the following outcomes of ASU were found in this study: attitude, performance and efficiency. There are sub triggers and outcomes because for example not all of the expected cognitive appraisals are triggers for ASU. A further description is given to fully understand these triggers and outcomes of ASU.
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5.1.1. Triggers for ASU
Novel situations
In this context the sub trigger of new task and changes in the system environment were only mentioned once. It can be argued that the more novel situations which a user encounters the more trial and error one experiences (Sun, 2012). However, in this post-adoptive situation, the system has been in place for a while and is less novel. Software development teams need to meet several demands of stakeholders (Faraj and Sambamurthy, 2006). This also influences their ASU behaviour. Five participants mentioned that the external environment, including changes in the market or a client request, triggers ASU. Thus, ASU is whiting the context of software development teams triggered by the external environment (including client requests).
P1: External environment acts as a trigger for software development teams to show ASU behaviour
Team context
As expected, the support from other people is important (Sun, 2012). However, less team contextual triggers appeared to influence ASU than expected. In this research communication came up as a trigger for ASU. Software development teams have a high level of collaboration (Faraj and Sambamurthy, 2006) for which extensive communication is needed. Moreover, software development teams use co-workers as a source of information (Jo et al., 2007). Internal communication triggers ASU behaviours within software development teams.
Only 2 users mentioned other’s use as a trigger which contradicts the research done by Sun (2012). It could be that communication and observations are closely related and therefore the observations did not come up.
Besides communication internal to the team, also external communication came up as a trigger for ASU. Participants mentioned that besides sharing experiences to team members also communicating with external relations and going to congresses or courses triggered ASU. Thus whereas the previous research was conducted among independent unknown respondents (Sun, 2012) it is important to consider the communication between team members but also external relations as a trigger for ASU. P2a: Communication within a software development team acts as a trigger for ASU.
P2b: Communication with non-team members acts as a trigger for ASU.
Leadership
29
Thus, a leader is not controlling a team but guiding them which makes sure that the focus is not on the expectations of a leader but gives room for creativity (Diliello and Houghton, 2006).
Innovative people may not be responsive to demands and may even resits it and have a strong orientation to autonomy (Sun, 2012). Moreover, groups may show increased innovative behaviour when they perceive a choice in accomplishing a task (Diliello and Houghton, 2006). This is also the case for software development teams. The results have shown that autonomy provided by the leaders enables the team to further explore the system. It is thus important to provide autonomy to a software development team to trigger ASU.
This research enhances the idea of when a leader acknowledges, is open to, and support ideas then employees would share more creative ideas (Diliello and Houghton, 2006). Thus, this open environment invites adaptation efforts like ASU (Beaudry and Pinsonneault, 2005). Whereas Sun (2012) found discrepancies as a trigger for ASU, this was only mentioned by one interviewee in a software development context. This participant has started working recently at this company and the system is rather new to her. Thus, discrepancies may trigger ASU in a post-implementation phase, but during the post-adoption phase, where the system has been in place for a longer period of time, this does not seem to trigger ASU behaviour.
Previous research (Sun, 2012) did not find support for facilitating conditions as a moderator for their 3 main triggers (Figure 2). Also in this research it was not found as support in terms of infrastructure as part of organizational support. However, respondents noticed the importance of available resources to the team, to be able to look for new features and the time needed. The leader could provide support in terms of resources (e.g. time, training) to explore a system. Even though it may seem like a large investment to provide training and give more autonomy, it may outweigh the benefits which an organization could gain by doing so (Beaudry and Pinsonneault, 2005).
P3a: A leader could trigger ASU by providing autonomy to team members.
P3b: A leader could trigger ASU by providing support for innovation to team members P3c: A leader could trigger ASU by providing resources to team members.
Personal Characteristics
30
Even though IT self-efficacy (ITSE) was suggested as a possible trigger by multiple researchers (Beaudry and Pinsonneault, 2005; Bruque et al., 2008), only three mentioned ITSE in this context. People with a high level of ITSE explore IT by actively searching for information (Wang et al., 2013). ITSE was not specifically mentioned by many respondents as a trigger but it may have occurred because there is evidence of active information search. Also ITSE is about the belief to be able to use an IT, since these participants develop software for others ITSE may be present and be self-evident. P4: Personal Innovativeness in IT acts as a trigger for ASU.
Knowledge
A new and surprising finding was knowledge. A source of knowledge could be experience. Experiences are stored in the memory on which a user could rely on (Sun, 2012). Previous research found prior use as an antecedent for post-adoptive behaviour (Jasperson et al., 2005). This study gave the same insight because people rely on past experiences with a particular system and thereby gained knowledge in which possible features are available.
Another surprising finding was the search for information. Even though as discussed this is mostly shown by people with ITSE (Wang et al., 2013) and this option may nowadays even more used due to the accessibility of information via the internet. Thus, the locus of innovation is to rely on himself to look for information which can be via the internet (Bagayogo et al., 2014).
P5a: Previous experience with a system acts as a trigger for ASU. P5b: The online information search acts as a trigger for ASU.
Cognitive appraisal
31 P6b: Dissatisfaction acts as a trigger for ASU.
Perceived Usefulness
A second expected and confirmed trigger is the Perceived Usefulness (PU). Many users try new IT applications expecting to have certain performance and/or personal related outcomes (Jasperson et al., 2005). It is reasonable that Perceived Usefulness is a trigger because people would spend time and effort in experimenting with an IT if it would benefit their performance (Wang et al., 2013). What a surprising new finding was that besides performance benefits that specifically effectiveness is an important perceived benefit and a trigger for ASU. Personal perceived benefits also came up as energy reduction and simplicity. This complex context of software development showed that employees are triggered by searching for easier ways to accomplish this difficult task. This trigger did not come up during previous research because it refers to a mature system (MS Office) which is overall seen as easy to use (Sun, 2012). This paper explored triggers in a complex context and participants mentioned the perceived benefits such as performance, effectiveness, energy reduction and simplicity as a trigger for ASU.
P7a: Performance as part of PU acts as a trigger for ASU P7b: Effectiveness as part of PU acts as a trigger for ASU P7c: Energy reduction as part of PU acts as a trigger for ASU P7d: Simplicity as part of PU acts as a trigger for ASU
5.1.2. Outcomes of ASU
Attitude
Besides triggers, also several outcomes of ASU were observed. Team effectiveness exists out of attitude and performance (Kuo, 2004). From the possible attitude outcomes only job satisfaction emerged as an actual outcome of ASU. Team or organizational commitment was not mentioned. P8: ASU improves the job satisfaction of software development teams
Performance
32 P9b: ASU improves the easiness of the work for the users
P9c: ASU provides a better overview for the users within the system
Efficiency
Adaptation efforts could be directed towards gaining benefits and therefore are likely to result in performance improvements (Bala and Venkatesh, 2016; Beaudry and Pinsonneault, 2005). Thus, whereas efficiency was mentioned as a trigger, it was also mentioned as an outcome. The active revision of a system in order to maximize your benefit from a system was expected to increase efficiency and effectiveness (Beaudry and Pinsonneault, 2005). This paper only showed efficiency as an outcome of ASU. Efficiency improvements were made by reducing errors and doing the work faster (Burton-Jones and Grange, 2013). There is a chance that mistakes are made when using a new system (Sun, 2012) but during this postadoptive stage new features led to reducing mistakes. The only part of efficiency which did not come forward was the reduction of rechecking. This could be explained due to the difference in context because users are familiar with the system and know whether to rely on it or not.
P10a: ASU improves the efficiency of completing tasks for users P10b: ASU reduces the amount of errors made
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6. Conclusion
This chapter reveals the major theoretical and managerial implications of this study. Finally a short conclusion is presented.
6.1. Theoretical implications
Previous research looked at IT applications as a black box and not as a range of features (Jasperson et al., 2005). However, ASU is focussed on the exploration or exploitation of features (Sun, 2012). This research analysed the features of a system which is considered as the components of an IT system (Jasperson et al., 2005). This study contributes to the literature by exploring the triggers and outcomes of ASU in a software development context. This context revealed other triggers and outcomes compared to previous research.
Moreover, previous research focussed on the implementation period or on the intention to use features. However, these are not the best predictors of actual usage in a post adoptive stage (Jasperson et al., 2005). This research contributed by focussing on actual usage during a post adoptive phase. This allowed the study to find out that whereas perceived threat and controllability triggered adaptation behaviour during the implementation phase, it is not the same case for post adoptive usage. Previous research focussed on more simplistic tools (MS Office) (Sun, 2012). This research contributes by exploring a different technological context (Sun, 2012; Beaudry and Pinsonneault, 2005). This led to the new insight of simplicity. Simplicity acts as a trigger for ASU but is also an outcome of ASU. This trigger and outcome did not come up during earlier research on ASU, but it did now perhaps due to the more complex technology. By exploring the usage within software development teams, the respondents were known to be knowledgable about IT.
Multiple researchers suggested to investigate the support from others (Bala and Venkatesh, 2016; Bruque et al., 2008; Sun, 2012) in terms of the team context (Beaudry and Pinsonneault, 2005; Nevo et al., 2016) or leadership (Beaudry and Pinsonneault, 2005; Bruque et al., 2005). This paper contributes by including team and leadership triggers for ASU. In addition, the influence of Perceived Usefulness has expanded. Besides the perceived benefits on performance, also effectiveness, energy reduction and simplicity are perceived benefits which trigger ASU.
This paper also contributes by mapping outcomes of ASU. There has not been literature on this and in this way this paper contributes to the theory.
6.2. Practical implications
34
When the right adaptation strategies are stimulated, it would result to improved job satisfaction, performance and efficiency. Employees who are more satisfied with their job would work longer for this company and may also go an extra mile. In addition, ASU benefits the performance of an organization in the sense of quality, contingency and overview. Efficiency can even be further expanded by making the work easier, reduction of errors and time. These positive outcomes make it possible for managers to encourage ASU and gain more value from installed IT systems.
Leaders or managers are also able to provide adequate resources to users so that they are able to take advantage of and adapt to the IT system (Beaudry and Pinsonneault, 2005). Also providing the autonomy to users anable them to experiment and innovate with IT. Leaders need to make sure that even though when resources are not available that they are open towards creative ideas because if this does not happen the creative behaviour of user will diminish. A leader should recognize employees who are willing to try out a new IT (PIIT). These employees are probably innovative adapters and could be used to share experiences with others during meetings (Yi et al., 2006). This triggers ASU by communication.
Moreover, the organization or a leader could show potential benefits of a new feature. This could also be communicated via a team member or by sending employees to congresses or courses to generate ideas. Also hiring external consultant triggers further ASU since they provide other experiences and share these.
6.3. Limitations and future research
35
information which is also shown by people with a high level of IT self-efficacy. Future research could indicate whether this would be a trigger for ASU.
6.4. Conclusion
36
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