A Behavioral View on the Antecedents and Contingencies of
Information Technology Decision-Making:
A Review, Synthesis and Future Research Directions
ABSTRACT
Despite the importance of information technology (IT) investments, theoretical and empirical examination attention within the IS field to date mainly focused on the consequences of them.
Therefore, there remains fuzziness about the underlying process, and our understanding of antecedents of IT decision-making remains underdeveloped. A highly influential theory to describe such organizational decision-making is A Behavioral Theory Of the Firm (BTOF), which
is perceived as under-utilized within the IS field. This paper uses the opportunity to fill both gaps, through reviewing the literature covering factors that influence IT decision-making and cite
BTOF. Deriving from a final sample of 41 articles, results show four overarching themes
consisting of three different dimensions of antecedents, of which each dimensions’ impact is dependent upon the managerial attention to IT. These themes are visualized accordingly on the
basis of a conceptual framework, which will be useful in guiding future research.
Key words: IT decision-making, Behavioral Theory of the Firm, IT investment, antecedents, information technology, IT strategy
Author: Thijs van Loenen | Supervisor: Dr. John Q. Dong | Co-assessor: Nicolai Fabian Student no: S3755428 | t.van.loenen.1@student.rug.nl | University of Groningen
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Table of Contents
1. Introduction ………2
2. Methods ………...5
2.1. Data Collection 2.2. Data Analysis3. Descriptive Analysis ………9
3.1. Journal Distribution 3.2. BTOF-utilization 3.3. Study Approach4. Thematic Analysis ………..13
4.1. Environmental Factors 4.2. Organizational Factors 4.3. Technological Factors 4.4. Managerial Attention to IT5. Discussion and Conclusion ……….40
5.1. Main Findings and Implications5.2. Limitations 5.3. Future Research
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1. Introduction
Lately, the role of IT has been going through a shift. Organizations invest billions of dollars in information technologies. Subsequently, estimates suggest that IT investments are among the most substantial elements of capital investments of many organizations (Boonstra, 2003; Ravichandran & Liu, 2011).Whereas in previous decades, new IT only affected the IT staff, it is nowadays affecting the organization as a whole (Majchrzak, Zammuto, Griffith,
Dougherty & Faraj, 2007). Investments in IT were seen as an enabler of competitive advantage, but are today becoming more or less necessities. Due to the COVID-19 virus, the use of IT became even more critical than ever (Coeckelbergh, 2020), and a majority of organizations are ‘forced’ to furtherly invest in digital strategies. COVID-19 is an - probably unique - example where one might observe that one initial factor, being the event of a virus as an environmental pressure, triggers IT investment.
However, a rich research body generally describes that more dimensions interplay in case of organizational decision-making, such as internal and technological factors. Within the context of IT, there are, amongst other instances, empirical examples showing that institutional pressures (Teo, Wei & Benbasat, 2003), financial performance (e.g. Hall & Liedtka, 2005; Anand, Sharma & Kohli, 2020), a firm’s related IT infrastructure (Renkema, 2003) and
proactiveness in IT strategic posture (Ravichandran & Liu, 2011; Xue, Ray & Zhao, 2017) could affect IT decision-making. IT decision-making within this study covers decisions related to investment, implementation, adoption, and usage of IT.
Despite the importance of IT, there remains fuzziness about the antecedents and contingencies that interplay before IT decision-making. Until this day, there is still a majority of research within the IS field that even tends to treat IT investments as a ‘given’ and mainly discuss the consequences of IT, thus focusing on performance impacts (Salge, Kohli & Barrett, 2015). However, there is generally a compelling decision-making process before organizations decide to invest in IT. Given the importance of IT decision-making outcomes on organizational success, it is critical to understand how they are effectuated.
BTOF by Cyert and March (1963) is a widely used foundation for describing and
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maximizing profits. This satisficing behavior is justified by their concept of bounded rationality: top management possesses limited cognitive capacity, information, and time to form the final decision (Cyert & March, 1963).
Despite its immense impact, there appear to be areas where the promise of BTOF has not been met (Argote & Greve, 2007; Gavetti, Greve, Levinthal & Ocasio 2012). Within their review of BTOF, Argote and Greve (2007) therefore, encourage scholars to use the implications of BTOF in developing theory and incorporate the social processes and contextual factors that affect organizational decisions.
As one might observe, there is a need to both solve the issue of lack of consensus on antecedents of IT decisions and the lack of application of BTOF. Therefore, the aim of this study is to identify articles discussing antecedents and contingencies of IT decision-making. Besides that, the articles should initially cite BTOF when describing the decision-making process. Accordingly, the following research question has been formulated:
RQ: How does the IS field utilize BTOF in describing antecedents and contingencies of
IT decision-making?
In order to address the gaps mentioned above and the research question, a systematic literature review in line with the methodology of Webster and Watson (2002) and Tranfield, Denyer, and Smart (2003) has been executed. After examining the exclusion and inclusion criteria, this study identifies 41 relevant articles. The final sample of articles consists of leading IS journals and predominantly cites BTOF. To address the diversity of research on decision-making towards IT, a thematic analysis has been executed. Correspondingly, the results from the studied articles were analyzed and synthesized. Three different dimensions of antecedents of decision-making towards IT were found. Based on the results, managerial attention to IT was found to play a moderator role between the three dimensions of antecedents and the decision to invest, implement, use, and adopt IT. These interrelationships were illustrated through a
conceptual framework.
Hence, the contribution of this paper is two-fold. This systematic literature review
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their role might be. Moreover, it provides an indication of the current state of application of
BTOF within the IS field.
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2. Methods
This paper’s strategy followed the systematic review method, as suggested by Webster and Watson (2002) and Tranfield et al. (2003). Although the usage of systematic reviews
predominantly occurs in the medical field, it has been found to be a useful method for social and management science as well (Tranfield et al., 2003). In line with that, Webster and Watson (2002) criticized the IS field for having a lack of quality literature reviews and advised to use it more often, in order to strengthen the IS as a field of study (Webster & Watson, 2002; Levy & Ellis, 2006). The method is unlike classic literature reviews since the researcher can synthesize the findings in “a systematic, transparent and reproducible manner” (Tranfield et al., 2003, p. 207). Synthesization is done in this particular circumstance through coding and identifying emergent themes. The aim of bringing together themes is to “explain how one piece of research builds on another” (Shaw, 1995, p. 326). From that point on, a conceptual framework and possible future research could be discussed.
2.1. Data Collection
The first step of the review was to determine the review scope and review protocol. The review method was produced in such a way that there was an ability to be creative within the review process whilst also ensuring less researcher bias (Tranfield et al., 2003).
After deciding on the review scope and protocol, a dataset was gathered in line with the suggestions of Webster and Watson (2002). The initial main sample was found through
searching the Web of Science-database. The Web of Science is a well-established research platform and often used for IS research. Since Cyert and March’s BTOF (1963) has a prominent role within this study, the sample should initially cite that book. The Cited Reference Search of the Web of Science enabled to search for papers citing BTOF (Cyert & March, 1963).
Furthermore, only top journals within the IS field were filtered on, also referred to as the AIS
Basket of Eight. This is in correspondence with the statement of Levy and Ellis (2006) that:
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European Journal of Information Systems, Information Systems Journal, Information Systems Research, Journal of the Association for Information Systems, Journal of Information
Technology, Journal of Management Information Systems, Journal of Strategic Information Systems, and MIS Quarterly. No time period was enabled since the aim is not to search for a
specific time restriction but to create an overarching view of the decision-making towards IT in the IS literature. This predefined set of filters resulted in 59 articles.
While searching for high quality literature is critical, it is also important to recognize whether articles apply to the study (Levy & Ellis, 2006). Because all articles in the sample were not necessarily related to the research scope, they needed to be critically assessed for their true relevance. For an overview of the assessment, in line with the visualization of Wolfswinkel, Furtmueller, and Wilderom (2013), see appendix A. Conforming exclusion and inclusion criteria, the titles and abstracts were reviewed and documented within Excel. The articles should
discuss information technology constructs and decision-making. It was assumed that all the articles discuss decision-making since they cite Cyert and March (1963); however, to prevent any errors, this inclusion criterion is involved as well. Regarding exclusion criteria, articles solely discussing consequences of IT investments and its practices were removed, since the scope of this research is antecedents and contingencies only. Whenever the title and abstract were too difficult to determine the relevance of the article, a full-text scan was needed to clarify further whether the article should be included or not. After scanning the full-text, the next step was to read the full article to clarify whether to include or exclude it.
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(2020) that were citing and using BTOF within their research, which was added accordingly. The final sample resulted in 41 articles, to be found in appendix B.
2.2. Data Analysis
In line with the proposals of Wolfwinkel et al. (2013), the next step after selecting the papers was to analyze them through coding. All the articles from the final sample were kept in both hardcopy and electronic format in order for them to be readily available. Initially, there has been made sure that each paper was read. Parts that could have a profound contribution in answering the research question were highlighted. After highlighting all of the relevant parts and re-reading them, a couple of concepts emerged. This process is often referred to as open-coding. These concepts are also based on prior literature and should be well defined in the IS field.
Moreover, the maturity of the literature on antecedents and contingencies of IT decision-making in the IS field was analyzed on the basis of a descriptive analysis, while also examining the use of BTOF. Mature research fields are generally recognized by studying a diversity of topics and applying multiple research methods, instead of focusing on one or a few (Cheon, Groven & Sabherwal, 1993). Therefore, the sample is additionally categorized and analyzed based on their usage of Cyert and March (1963), publication year, study design, limitations, and research scope. The examination of the usage of BTOF illustrates the extent to which the papers use the theory of Cyert and March in explaining IT-decision making. It follows a similar categorization as Roberts, Galluch, Dinger, and Grover (2012) used for their research (Table 1).
Table 1: Utilization of BTOF, following Roberts et al.’s (2012) categorization. Referenced as
background or minor citation
Articles in this category cite a concept of Cyert and March and only use it as an illustration (for example, within their introduction or background). It has not been used for any propositions or hypotheses whatsoever.
Provides theoretical support
Here, articles in this category cite a concept of Cyert and March to support or develop a proposition or hypothesis and are thus part of the research scope.
Used in research model In this category, articles use a concept of Cyert and March as their proposition or hypotheses within their research model.
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Afterward, through axial coding, the concepts identified within the first step of coding were subdivided into several ‘key categories’, and interrelations between the categories were noted. The last step in coding was to ‘fit the puzzle’ (Wolfswinkel et al., 2013) and define the
relationships that could ultimately lead to a conceptual framework. Accordingly, a concept matrix (Webster & Watson, 2002) has been used to help visualize the classification of themes. Articles should at least use citations that cover a specific theme, in order for them to be included within a theme. Ever since there was a need to interrelate qualitative and quantitative data, it was
conceived as the most appropriate way to make sense of the findings and guide future
research. It is often used to systematically categorize content of text and identify relationships between the themes (Berg, 2004).
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3. Descriptive Analysis
The first section of the findings covers the descriptive statistics, which helps us identify the characteristics of the study sample. Accordingly, the distribution of journals, utilization of BTOF, and the distribution of study approaches will be examined and discussed.
3.1. Journal Distribution
Figure 1 depicts the distribution of the number of articles published by each journal. The most dominant journal within this sample could be considered the MIS Quarterly. As one might notice, no article from the Journal of Association for Information Systems discusses both BTOF and antecedents or contingencies of IT decision-making or has a profound contribution towards it. Two of the added articles through snowball sampling were from the journals Decision
Sciences and Accounting Review and thus explained their small input. These are the only
exceptions of journals that were not part of the AIS Basket of Eight.
Figure 1: Distribution by journal.
0 2 4 6 8 10 12
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3.2. BTOF-utilization
The earliest publication identified within this review scope was that of Huber (1981). After that, there is a considerable gap in the utilization of BTOF in the context of IT. This might be due to the fact that IT itself gained more popularity in the earlier 90s, ever since studies were finding positive relationships between the use of IT and higher performance. When we inspect the distribution of the publication of the articles over the years, it is observable that the BTOF and IT simultaneously gained popularity since the later 90s. However, the extent to which the authors adopt BTOF within their study differs significantly. As the pie chart below illustrates, a majority of the articles utilize BTOF as theoretical support to develop the logic for their
propositions or hypotheses. Another considerably large share only referenced BTOF in the background or implemented it as a minor citation. The articles that did apply BTOF as a more profound element of their study were using it in their research model (Huber; 1981; Renkema, 1998; Ang & Straub, 1998; Anand et al., 2020) or as a theoretical base (Salge et al., 2015). The study from Salge et al. (2015) is therefore considered one of the key articles within the sample regarding coverage of BTOF. Although the non-referencing articles (referred to in figure 3 as ‘not’) are explained through the process of ‘snowball sampling’, one could still conclude that the
BTOF remains underutilized in the context of describing IT decision-making, or even the IS field
in general. Another vital insight from this analysis is that in recent years, the articles started implementing the BTOF within their research model more extensively.
Figure 2: Times cited BTOF by year.
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Figure 3: Distribution of extent of utilization of BTOF.
3.3. Study Approach
The most dominant study approach is of quantitative nature. However, qualitative analysis also has a relatively large share in it. This is in line with the statement of Okoli (2015) that “although information systems was initially a research field with primarily quantitative analysis, qualitative research has now established equal regard, if not yet equal frequency, of application” (p. 900).
The amount of articles covering literature reviews or conceptual reviews is relatively large as well. These articles do not discuss the exact same concepts or have a different research scope compared to this review. For example, Huber (1981) discusses organizational decision making in general and the role of decision support systems with regards to that. However, they discuss articles that were published before the definite rise of popularity in IT. Furthermore, another insight is that, since the request of Webster and Watson (2002) for more systematic literature reviews, there has not been a severe increase in the number of review publications within this particular sample.
Lastly, only one article used a mixed-method approach (Yap et al., 1994). This finding could motivate IS scholars to use that approach more frequently.
These findings illustrate that a meta-analysis was impossible since there are many variations between the primary studies because of their mixed nature of methods. Therefore, combining results from them through a meta-analysis would not give sensible results.
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4. Thematic Analysis
Next within the findings are the results of the thematic analysis. At first, the concept-matrix (Table 2) will be shown, which helps visualize howthe constructs were classified into four overarching themes: environmental factors, organizational factors, technology factors, and managerial actions towards IT. Further on within the thematic analysis, the conceptual framework will be introduced, and each theme will be discussed individually.
Table 2: Concept Matrix: Themes explaining IT decision-making
Environ-ment Organization Tech-nology Manager Attention to IT Article Methodology Un cert a in ty D yn a mis m In st itutio n a l pre ss u res Financial P er form ance Or g a n iz a tion a l cu lture P ol itic s User ch a ra cte ristics Fi rm siz e In d u st ry type IT ca p a b ilities IT inf ra st ru ctu re IT S tr a tegic P ostu re Bu siness -IT ali g n m ent IS P la n n in g
Huber, 1981 literature review x x x x x x
Yap et al., 1994 mixed-method x x x x
Karimi et al., 1996 Quantative x x x x x x x Sillince & Mouakket, 1998 Qualitative x x x x Lyytinen et al., 1998 literature review x x x Renkema, 1998 Qualitative x x x x x Mendelson & Pillai, 1998 Quantative x x x
Ang & Straub, 1998 Quantative x x Dewan et al., 1998 Quantative x x Rouibah & Ould-ali, 2002 Qualitative x x x x Templeton et al., 2002 literature review x x x x
Teo et al., 2003 Quantative x
Serafeimidis & Smithson, 2003
Qualitative x x x x x x x x
14 Environ-ment Organization Tech-nology Manager attention to IT Article Methodology Un cert a in ty D yn a mis m In st itutio n a l pre ss u res Financial P er form ance Or g a n iz a tion a l cu lture P ol itic s User ch a ra cte ristics Fi rm siz e In d u st ry type IT ca p a b ilities IT inf ra st ru ctu re IT S tr a tegi c P ostu re Bu siness -IT ali g n m ent IS P la n n in g Swanson & Ramiller, 2004 literature review x x x x x x Hall & Liedtka,
2005
Quantative x x x x x
Wang et al., 2006
Quantative x x x x x x
Kwon & Watts, 2006
Quantative x x x x
Son & Benbasat, 2006
Quantative x x x x x x x Lyytinen &
Newman, 2008
literature review x x x x
Xue et al., 2008 Qualitative x x x x
Kobelsky et al., 2008 Quantative x x x x x x Meissonier & Houzé, 2010 Qualitative x x x x Leonardi, 2011 Qualitative x x Ravichandran & Liu, 2011 Quantative x x x x x x x x x x x Pillay et al., 2012 Qualitative x x x x x Bradley et al., 2012 Quantative x x x x x x x x
15 Environ-ment Organization Tech-nology Manager Attention to IT Article Methodology Un cert a in ty D yn a mis m In st itutio n a l pre ss u res Financial P er form ance Or g a n iz a tion a l cu lture P ol itic s User ch a ra cte ristics Fir m siz e In d u st ry type IT ca p a b ilities IT inf ra st ru ctu re IT S tr a tegic P ostu re Bu siness -IT ali g n m ent IS P la n n in g Hedman & Henningsson, 2016 Qualitative x x x x x Masli et al., 2016 Quantative x Dawson et al., 2016 qualititative x x x x
Moe et al., 2017 Qualitative x x x
Xue et al., 2017 Quantative x x x
Swanson & Burton, 2019
literature review x x x
Baker & Singh, 2019 literature review x x x x x x x Anand et al., 2020 Quantative x x x x x x x sum 17 13 20 13 9 13 23 9 5 19 12 20 9 12
In order to justify the data in a structured way, it has been incorporated into a synthesis, presented in the form of a conceptual framework, as seen in figure 5. Of the four overarching themes, three were identified as domains of the underlying antecedents. Managerial attention to IT is argued to play a moderator role between the domains and the decision to invest,
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Figure 5. Conceptual framework.
4.1. Environmental factors
A majority of the articles note the environmental context as a factor describing investments towards IT. As Lytinnen and Newman (2008) show within their framework, the environmental context (as well as the organizational context), such as critical events concerning organization's social, economic, political, regulatory, and competitive characteristics, shape the IS change. Furthermore, Kwon & Watts (2006) state that: “As IT managers and their corporate partners go about selecting IT initiatives to invest in, they need to understand the role that the environment can play in the success of these initiatives” (p. 328). Boonstra (2003) also illustrates this by providing the example of an economic downturn, when many organizations become less innovative and adopt a “wait-and-see approach” towards IS investments in particular. Although the environmental characteristics are considered a decisive factor in explaining IT investment behavior, it has not gained that much attention from researchers, according to Ravichandran and Liu (2011). Scholars within the sample tend to use different definitions and factors to describe the impact of the environment.
Environmental dynamism
. One factor that is often mentioned as a way to describe the impact of the environment is its dynamism. A dynamic environment is17
1998)” (Kwon & Watts, 2006, p. 328). Authors researching this phenomenon refer to it in various ways: clock speed (Mendelson & Pillai, 1998; Ravichandran & Liu, 2011), industry turbulence (Rouibah & Ould-ali, 2002; Boonstra, 2003; Mithas, Tafti & Mitchell, 2013), market volatility (Son & Benbasat, 2007) industry concentration (Dewan, Michael & Min, 1998; Kobelsky, Richardson, Smith & Zmud, 2008) and industry information intensity (Ravichandran & Liu, 2011). However, scholars often use the word ‘dynamism’ when describing it. The main rationale a majority of these scholars agree upon is that environmental dynamism, or aspects of environmental dynamism, entail more information to process and therefore increase the need to use IT.
Mendelson and Pillai (1998) were one of the first authors to study environmental dynamism (“clock speed”) in the context of investing, using or adopting IT. They explain that “dynamic environments generate more information that the organization must take into account in its decisions. Hence, one response to increasing dynamics is to increase the organizational ‘bandwidth’ through the use of better information and communication technologies” (Mendelson & Pillai, 1998, p. 416). The study results correspond with this statement, showing a strong relationship between clocks peed and the use of IT, especially IT which facilitates real-time communication between team members, suppliers, and customers. Above that, Mendelson and Pillai (1998) found that firms operating in environments with higher dynamism were likely to prevent that decision-makers would cope with ‘information overload’ because of such
environments. Despite the relevance of their results, the study should be taken into account with caution, since it focuses on the IT industry. The study of Ravichandran and Liu (2011) finds similar positive results regarding industry clock speed. Their findings show that a higher industry clock speed enhances IT intensity. However, they also found that it does not necessarily
enhance managers’ proactiveness. The article of Mithas et al. (2013), on their hand, found that greater industry turbulence enhances the degree to which an ‘IT strategic posture’ has a
divergent influence on IT investment in general. However, in case of outsourcing, there was little to no moderating effect (Mithas et al., 2013). Ravichandran and Liu (2011) also researched the impact of information intensity on IT intensity and manager’s proactiveness, finding only a positive relationship between the information intensity and proactiveness. There were also studies finding positive results on the relationship between industry concentration and IT
investment (Dewan et al., 1998; Kobelsky et al., 2008). Both studies find evidence that a higher industry concentration leads to higher IT budget levels and therefore increased the incentive to invest in IT (Dewan et al., 1998; Kobelsky et al., 2008). Lastly, without finding empirical
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stating that “high levels of environmental dynamism promote the development of an emergent IT strategy" (Baker & Singh, 2019, p. 8).
Controversially, there is one article (Kwon & Watts, 2006) which considers the impact of environmental dynamism to be less decisive. The findings of Kwon and Watts (2006) might even entail that managers do not take implications of the dynamism of the environment into account when deciding whether to invest in IT or not (Kwon & Watts, 2006). However, despite these dubious findings, the above reasoning still suggests that:
P1a: The higher the dynamism of the environment, the more likely a firm will invest, implement, adopt, and use IT.
Environmental uncertainty
. The second environmental factor is its uncertainty. It goes hand in hand with environmental dynamism, as a higher perceived dynamism increases uncertainty (Kwon & Watts, 2006). A majority of researchers describe that as a response to unpredictable environmental contingencies, firms will invest in IT to cope with a higher demand of coordination of decision-making (Karimi, Gupta & Somers, 1996; Kobelsky et al., 2008), in order to scan the environment more effectively (Huber, 1981; Rouibah & Ould-ali, 2002; Constantiou & Kallinikos, 2015; Anand et al., 2020) or as an ‘act of faith’ (Serafeimidis & Smithson, 2002; Boonstra, 2003).Firstly, studies are describing that increased uncertainty entails greater demands for firms to coordinate decision-making more precisely (Karimi et al., 1996; Kobelsky et al., 2008). According to them, this will eventually lead to a richer IT-enabled information process. The paper of Kobelsky et al. (2008) is actually the only paper researching environmental uncertainty as an element of their research model. Therefore, their empirical evidence that environmental uncertainty has indeed a positive influence on IT budget levels is even more insightful.
Secondly, a number of articles describe that environmental uncertainty triggers the need for organizations to scan their environment more effectively. Organizations might invest in decision-support-systems (Huber, 1981) or business intelligence analytics (Rouibah & Ould-Ali, 2002; Constantiou & Kallinikos, 2015; Anand et al., 2020). Rouibah and Ould-ali (2002)
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significant influence on the extent to which firms are able to assess, analyze and address trends within the environment (Constantiou & Kallinikos, 2015; Anand et al., 2020).
Third, Serafeimidis and Smithson (2002) argue that, because of the uncertainty of the environment, organizations might be unaware of the consequences of environmental
contingencies and therefore come along with ‘act of faith’-decisions. Renkema (1998) actually refutes that by stating that ‘act of faith’-decisions are becoming less common, ever since senior management is becoming highly unreceptive towards such decisions. On the other hand, following the reasoning of Serafeimidis and Smithson (2002), one could consider that with or without the need for environmental scanning, in case of high environmental uncertainty, firms are either way likely to invest in IT. Therefore, the above reasoning suggests that:
P1b: The higher the degree of uncertainty of the environment, the more likely a firm will invest, implement, adopt, and use IT.
Institutional pressures
. The third environmental factor is identified as the institutional pressures firms perceive. As Teo et al. (2003) state, firms are “subject to pressures to beisomorphic with their environment” (p. 21). Both conceptual research (Swanson & Ramiller, 2004) and much empirical research (e.g., Teo et al., 2003; Salge et al., 2015) showcase the influence of institutional pressures on IT decisions. The article of Teo et al. (2003) could be considered the most influential in describing a connection between institutional theory and the adoption of IT. They initiated it because of the profound belief that "the decision to adopt may have more to do with the institutional environment in which a firm is situated than rational intra-organizational and technological criteria" (Teo et al., 2003, p. 20). It researches the three isomorphic processes classified by DiMaggio and Powell (1983): mimetic, coercive, and normative isomorphism.
Mimetic. As DiMaggio and Powell (1983) describe, mimetic isomorphism is the process
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Teo et al. (2003) refer to studies finding implicit evidence that firms imitate successful IT innovators out of competitive necessity. However, these studies did not examine a direct relationship. Therefore, Teo et al. (2003) were among the first to research a direct relationship between mimetic pressures and adoption of IT, through studying financial electronic data interchange (FEDI). The results of the study show a significant positive influence of mimetic isomorphism on the intention to adopt FEDI. Furthermore, mimetic influence appears to be more persuasive when the technological innovation is perceived as complex through the eyes of the decision-makers within an organization (Teo et al., 2003).
Many scholars within the sample agree with the findings of Teo et al. (2003). Boonstra (2003) states that one of the external dominant forces that influence IS decisions, is IS-related initiatives of competitors. Swanson and Ramiller (2004) refer to this as ‘bandwagon
phenomena’, when organizations join a stampeding herd, without reconsidering involvement of high costs or risk. In their study, they distinguish between mindfulness and mindlessness: “To justify adoption then, the mindless firm may be content with the rationale that ‘everyone is doing it’ or the justification that ‘it’s time to catch up’” (Swanson & Ramiller, 2004, p. 564). Son and Benbasat (2007) use a similar approach as Teo et al. (2003) when researching the extent of adoption and use of IT due to, amongst others, institutional pressures. Their results show that mimetic pressures and their sub-constructs have a significant impact on the adoption intention of B2B e-marketplaces. However, they did not have a significant impact on the level of
participation (Son & Benbasat, 2007). Furthermore, Kobelsky et al. (2008) argue that senior managers often dedicate the level of IT-budget based on the industry average instead of a variety of firm or contextual factors. Moreover, the study of Singh and Phelps (2013) researches how organizations decide for a specific OSS license. They state that “the likelihood that a new OSS project adopts a particular license increases when more role equivalent OSS projects have previously adopted such a license and when these projects are large and successful” (Singh & Phelps, 2013, p. 556). Their results show that social influence portrays a more prominent role than economic indicators when choosing a particular license. Lastly, Anand et al. (2020) argue that in making success and failure judgments, management tends to base their opinions on prior performance of reference groups, such as industry competitors or peers.
However, there is also an indication that there are limits to the mimetic process. Adopters with deep and diversified experience are less likely to adhere to what industry
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Mithas et al. (2013) showcase a different view of approaching the influence of mimetic isomorphism. They believe that in case firms do not take any social influence into account, there are two reasons justifying why. Firstly, the ways they analyze their industry are insufficient, and there are therefore weak signals. This corresponds with the statement of Cyert and March (1963) that choices from the industry participants should be visible. Secondly, their digital strategies could be independent of what competitors decide to do, mainly because of their internal efficiency and effectiveness (Mithas et al., 2013). Furthermore, they doubt that in case mimicry occurs, that it is solely arising from a desire following a ‘fashion sense’. It could also be based upon obsolete technology or products, if they don’t act upon their competitors (Mithas et al., 2013).
Coercive. The second isomorphism type characterizes the coercive pressures
organizations experience. Di Maggio and Powell (1992) state that coercive isomorphism derives from informal and formal pressures by other organizations, upon which the focal organization is dependent. Because of these pressures, organizations feel the necessity to meet cultural expectations and regulations (Maggio & Powell, 1992). Xue, Liang, and Boulton (2008) even state that due to coercive pressures, organizations are “forced to implement a certain
information technology” (p. 72).
Governmental regulations are the most common way through which organizations experience coercive pressures, as multiple scholars (Yap, Thong & Raman, 1994; Sillince & Mouakket, 1998; Bradley et al., 2012; Salge et al., 2015; Moe et al., 2017) describe. Yap et al. (1994) researched the Small Enterprise Computerization Programme (SECP), an initiation launched by the government of Singapore to promote the use of IT (or within the article referred to as ‘computerization’) within small businesses. This program aimed to mitigate resource constraints of small businesses by allowing them to receive incentives in the form of financial subsidies and technical assistance. Despite not being part of the initial research framework, the question was raised whether SECP-companies invested in IT because of the governments’ provision. 75% of the companies indicated that the subsidies and technical assistance had a large influence on their decision to invest in ‘computerization’ (Yap et al., 1994). Although the research has been held during the rise of the popularity of IT, it is still an appealing illustration of how the support or regulations of the government could potentially play a vital role in deciding to adopt IT. Furthermore, the study of Sillince and Mouakket (1998) shows how the University
Grants Committee (UGC) encouraged universities to enhance their information systems. The
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interesting example is the article of Moe et al. (2017), which researches to what extent public entities need to adhere to strict regulations in case of procurement of a new information system. Public entities are facing a challenge to simultaneously meet their own demands and adhere to strict regulations. Above of that, these regulations seemed to fluently change (Moe et al., 2017). Controversially, the results of Moe et al. (2017) show that procuring entities often act upon their own interest, despite any strict regulations. Another study illustrating coercive pressures is that of Bradley et al. (2012), who discuss governmental regulations in the context of hospitals. They show empirical evidence that hospitals experience a reformation of their healthcare, mostly due to governmental regulations encouraging the use of IT. Salge et al. (2015) also discuss
regulative legitimacy in the context of hospitals. However, they consider the regulative legitimacy to play a moderator role between four search mechanisms (problemistic search, slack search, institutionalized search, and mimetic search) and IS investment intensity. Salge et al. (2015) expect regulative legitimacy to influence organizational search predominantly in industries coping with severe regulatory oversight.
Whereas the above articles mainly discussed the impact of regulations, Teo et al. (2003) believe that coercive pressures primarily emerge because of dominant suppliers, customers or a parent corporation. Keeping in mind, as they state, that this assumption is in the context of financial data electronic interchange adoption. They present evidence of previous articles describing the influence of coercive pressures on FEDI adoption, as well as empirical evidence of their own. Their empirical evidence put parent corporations on the foreground as a major influencer on the intention to adopt IT. Trading partners were less decisive regarding their influence.
Xue et al. (2008) also found other constructs of coercive pressures that impacted the use of IT. Their results show that many respondents based their IT decisions on their partners. For example, a project leader of an affiliated hospital of a university mentioned that the
university needed to report to higher authorities, who were changing their reporting system from time to time. In order to meet the changing systems, they needed to continuously improve their human resource management applications (Xue et al., 2008).
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contradicting results could be related to the different characteristics of the technologies (Son & Benbasat, 2007).
Normative. The last isomorphic classification of Di Maggio and Powell (1992) is the
normative isomorphism. Normative isomorphism stems from educational pressures or pressures from professions (Di Maggio & Powell, 1992). If two organizations frequently communicate with each other, they are more likely to behave similarly because of their profession (Teo et al., 2003). Out of the three different classifications of isomorphism, the normative pressures brought about the most significant results in the study of Teo et al. (2003). Their statement illustrates normative isomorphism in practice: “these standards of behavior are diffused by key institutions that provide forums for information exchange, set standards, provide education, conduct
promotions and evaluate success of practices in professional and trade magazines” (Teo et. al, 2003, p. 24). Their results showed that norms brought about by business or professional circles were of a major influence when making decisions. Presumably, because of the atmosphere that these businesses or professional circles create, where other EDI adopters have a high
reputation. According to their findings, norms arising from customers were more impactful than norms arising from suppliers, which could imply that organizational decision-makers tend to be more customer-oriented (Teo et al., 2003). Other scholars find similar positive results on the impact of normative isomorphism. The study of Son and Benbasat (2007) found that, besides mimetic pressures, normative pressures portrayed a significant role in the intention to adopt B2B e-marketplace. However, same as for mimetic pressures, these findings were insignificant for the level of participation (Son & Benbasat, 2007). Moreover, through case studies,
Renkema (1998) found that the organizations studied seemed to adhere to certain standards within organizations’ professional circle, when investing in software packages. Furthermore, Ravichandran and Liu (2011) illustrate that, in case of a high information intensity, using IT to collaborate with value chain partners might also represent an expectation of the industry to adhere to and therefore be considered a norm. Their findings show similar results, since they show that business partners seem to influence a decision to adopt technologies actively.
Lastly, the studies of Mithas et al. (2013) and Xue et al. (2017) showcase how
organizational decision-makers base their decision on what is perceived as the industry norm. Managers often tend to focus on what the industry norms are, such as the degree of IT
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industry norm, as both articles (Mithas et al., 2013; Xue et al., 2017) refer to as strategic posture, of which later on within this study will be elaborated upon.
Despite some mixed findings, a majority of the studies illustrate how mimetic, coercive, and normative pressures each independently or simultaneously influence the decision to invest, adopt and use IT. Therefore, we propose that:
P1c: The stronger the mimetic, coercive and normative pressures, the more likely a firm will invest, implement, adopt, and use IT.
4.2. Organizational Factors
The second dimension of antecedents towards IT-decisions is located in the internal context: the focal organizations’ characteristics. Traditional IS conception tends to focus on technological and financial aspects, while often neglecting the organizational context and its underlying process, which are critical in successfully applying IT (Serafeimidis & Smithson, 2003; Karimi et al., 1998). Despite that, a majority of the articles from this sample seem to take the organizational factors into account when researching IT practices (e.g., Xue et al., 2008; Kobelsky et al., 2008). This could be due to a rise of popularity of the IT governance concept, which is generally defined as “controlling the formulation and implementation of the IT strategy via organizational structures and processes that produce desirable behaviors, which will ensure that IT initiatives sustain and extend the organization’s strategy and objectives (De Haes and van Grembergen, 2004; Weill, 2004; Weill & Ross, 2004)" (Bradley et al., 2012, p. 157). IT governance thus focuses on how IT should be structured internally. Scholars discuss different organizational factors, but the following appeared to be the most common.
Financial performance
. The financial state of an organization has been frequently mentioned as an antecedent to IT practice. Either because of poor financial performance (e.g., Hall & Liedtka, 2005) or the opposite of that, financial slack (e.g., Salge et al., 2015). In the context of Cyert and March (1965), poor financial performance is often referred to as problemistic search, whereas financial slack is referred to as slack search.25
poor performing firms are experiencing a worrisome dilemma: within financial means, they cannot increase their spending on IT practices. However, competitively, they will stay behind if they will not do so (Renkema, 1998). Generally, if firms have access to fewer financial
resources, it becomes more evident for them to make IT investments on the basis of hard numbers as well (Kwon & Watts, 2006). Further empirical evidence supports the statement that performance shortfalls indeed motivate to invest in IT (Bradley et al., 2012).
Some articles also describe the aspect of cost savings as a means why organizations invest more in IT practice (Renkema, 1998; Serafeimidis & Smithson, 2003; Son & Benbasat, 2007). Son and Benbasat (2007) found that organizations adopt and use B2B e-marketplaces based on the expectation to become more efficient within their transactions. Renkema (1998) described the role of cost pressures, which were presumably dominating IT decisions. An incentive to save on operational or transactional costs might eventually lead to outsourcing IT, as Ang and Straub (1998) cite: "a firm will choose to outsource or insource based on the comparative costs of internalizing versus the price it has to pay vendors for the same IS services (Saarinen and Vepsalainen, 1994)" (p. 537).
Opposite of that, are organizations with excess resources, often referred to as financial slack (Cyert & March, 1963). Since IT investments can promote social prominence and public prestige, managers are often persuaded to determine their slack resources to IS practices (Ang & Straub, 1998). Furthermore, Mithas et al. (2013) state that: “managers have incentives to invest free cash flow on resources under their control rather than pay out the free cash flow to shareholders, even if the investments are not cost effective” (p. 522). Some articles find empirical evidence for slack search in the context of IT. Kobelsky et al. (2008) show that firms with greater resources, on the basis of higher operating profit and lower leverage, invest more in IT. The study of Salge et al. (2015) shows that decision-makers make use of excess financial resources when investing in IS. However, their results suggest that this only occurs when they are confronted with legitimacy threats.
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suggests that poor performance or financial slack, despite their importance of contribution to the final decision, could affect decision-making regarding IT. Therefore, there is argued that:
P2a: In case of poor performance or (opposite of that) financial slack, firms are more likely to invest, implement, adopt, and use IT.
Organizational culture.
The culture of an organization, or in other words, the norms and values within an organization, is an often neglected feature within this research scope. Nonetheless, it does determine how innovative or risk-averse an organization behaves. In distinguishing between risk-averse or innovative organizations, Swanson and Ramiller (2004) typify organizations as either ‘mindfulness’ or ‘mindlessness’ organizations. The extent to which the organizational culture facilitates either innovative or risk-averse behavior affects the decision to invest, implement, adopt, and use IT.Only a minority of articles do dedicate a decisive role to organizational culture. For example, Boonstra (2003) observed that next to the characteristics of an IS problem, the culture of the organization was of vital importance as well. According to him, some organizations seem to have a more innovative attitude, especially towards IS. On the other hand, there are also organizations perceiving IS more or less as a necessity (Boonstra, 2003). This also determines whether organizations are perceived as laggards or innovators (Ravichandran & Liu, 2011). Within a different context, the study of Hedman and Henningsson (2016) found that the adoption of green IS is often contingent on organizational values, which was supporting previous
research. However, although their results are applicable to IS in general, the sustainability-perspective should be taken into account (Hedman & Henningsson, 2016). Furthermore,
Bradley et al. (2012) brings organizational culture to the foreground by adding it as an additional factor to the internal factors originated from the study of Xue et al. (2008). Bradley et al. (2012) believe that the organizational culture is of equal, or maybe even more importance.
Furthermore, they state that “an organization’s level of entrepreneurial focus contributes significantly to their use of IT” (Bradley et al., 2012, p. 160), based on their previous studies. The results from the focal study show that the extent to which their entrepreneurial norms and values explain the variance in commitment to IT governance (Bradley et al., 2012).
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risk-averse (Bozeman and Bretschneider, 1986; Dawson et al., 2016). Therefore, organizational culture might also sometimes be perceived as a barrier. Pillay, Hackney, and Braganza (2012) support this within their results, arguing that culture was conceived as problematic and not always supportive of organizational change. The study of Wang, Klein, and Jiang (2006) also agrees upon this way of thinking and found that cultural barriers can be ambiguous when deploying a complex technology. Especially in case of ERP-systems (Enterprise Resource Planning-systems), the focus of their study, which could consist of a variety of social elements. Accordingly, they found that organizations should take cultural differences into account when adopting ERP-systems (Wang et al., 2006). Therefore, the above reasoning suggests that:
P2b: In case of having a culture which is more receptive towards IT, firms are more likely to invest, implement, adopt and use IT.
Politics
. Many scholars tend to describe the process of decision-making towards IT practice as a political process. To illustrate this, Boonstra (2003) for example, states that “there is strong evidence that political activities play an important role in many IS decision-making processes” (p. 204). Participants within the decision process have divergent goals (Huber, 1981), trying to reach mutual consultation (Renkema, 1998; Boonstra, 2003). Fruitfulcollaboration between the stakeholders is important to create support for a decision (Renkema, 1998). Xue et al. (2008) and Bradley et al. (2012) propose that the CIO and other top
management personnel should be in a position of authority to influence the IT decision-making process, also referred to as IT function power. Despite the power of the final decision-makers, all participants should be held accountable for the consequences (Xue et al., 2008). As Teo et al. (2003) state, many costly investments in IT with an impact on the organization as a whole would have not been accomplished without a collective decision. However, stakeholders try to influence the decision-making process in order to reach outcomes that will satisfy their own interests (Boonstra, 2003). Especially in case of ERP-systems, where outcomes impact the whole company, the implementation is a highly political and social process (Wang et al., 2006). The individuals’ interests are often based on hierarchical and capability elements, which are likely to be redistributed accordingly after IT implementation (Meissonier & Houzé, 2010). IT could give employees more power, but at the same time give certain employees less autonomy, which might cause resistance, as discussed in the study of Meissonier and Houzé (2010).
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2012) or judgment devices in the form of heuristics, experts, networks or sponsors (Shollo, Constantiou & Kreiner, 2015). Through these political instruments, organizations are able to structure and smoothen the decision-making process. It also enables them to put IT on the organizational agenda more prominently. Based on the above reasoning, there is proposed that:
P2c: Before the decision to invest, implement, use, and adopt IT, firms are likely to undergo a highly political process.
User characteristics
. Today, only a few people fulfill their day-to-day work without the use of information technologies. Although many earlier studies viewed this differently, most of the users have an impact on how these technologies are implemented and used within an organization.At first, technologies are becoming more customizable to the needs of users (Leonardi, 2011). Therefore, organizations often choose to invest in information technologies that
complement the routines, capabilities, and needs of users (Lytinnen & Newman, 2008;
Leonardi, 2011). Scholars repeatedly refer to this phenomenon as sociomateriality, and some of the articles cover this subject (Lytinnen & Newman, 2008; Leonardi, 2011; Gaskin et al., 2014; Swanson, 2019). As Gaskin et al. (2014) describe, "sociomaterial turn draws attention to the way that digital technologies are intrinsically embedded into the fabric of local practices and conditions” (p. 850). The premise of this concept is that technology should not be treated as an exogenous force, but instead, there is a constant process of imbrication of people and
technologies (Leonardi, 2011; Gaskin et al., 2014). The article of Swanson (2019) builds upon this by arguing that technology should be seen as a routine capability, instead of a device.
Another view on this, is based on ‘previous programming’ (Huber, 1981), or in other words, prior experience. Organizations often base their investments on the previous experience of the employees who will use the IT. The way prior experience with IT and IT capabilities of the users influence the decision-making process will be further elaborated upon under 4.3.
Technology Factors. In line with the concept of organizational learning, the success of
implementation is partly determined by the extent to which executives learn from their prior experience (Templeton et al., 2002). As Templeton et al. (2002) further argued, preliminary research already showed that organizational learning is able to severely improve IT
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(2003) mention that a lack of learning ability could lead to IT failure. Furthermore, the study of Serafeimidis and Smithson (2003) shows that IS evaluation could help organizations improve their learning. Especially during IS change, characterized by uncertainty and risk, the
relationship between evaluation and learning is enhanced (Serafeimidis & Smithson (2003). However, Constantiou and Kallinikos (2015) degrade the influence of organizational learning and prior experience. They argue that organizations cannot solely take prior successes or failures into account when making decisions in case of a dynamic environment.
Another vital user characteristic, influencing the IS-decision process, is the extent to which users are supportive of changing the organizations’ IT infrastructure. This is often
explained by the well-known acceptance models (e.g., Venkatesh et al., 2003), as illustrated by Meissonier and Houzé (2010). Especially in case of outsourcing IT, gaining user support is difficult, as the study of Wang et al. (2006) clarifies. Unlike in-house IS projects, in case of IT outsourcing, users participate later within the process. Therefore, the systems are oftentimes only open to negotiate about redefining it (Wang et al., 2006). Users could also not be
supportive of change because they perceive threats to their current values or power due to the expected IS change (Meissonier & Houzé, 2010). Without a positive attitude of the users toward the system and willingness to participate in the implementation process, it might cause that employees would not learn or even an IT implementation to fail (Wang et al., 2006).
Since the above findings illustrate that user characteristics might be either a barrier or an advancement in the IT-decision making process, we suggest that:
P2d: The more supportive the user characteristics, the more likely a firm will
invest, implement, adopt, and use IT.
Firm size.
According to transaction cost theory and dependency theory, firm size is one of the most influential organizational characteristics that alter a firm’s behavior, especially in case of new environments (Karimi et al., 1996). Smaller firms tend to cope with resource constraints and a lack of expertise (Yap et al., 1994). Therefore, based on economies of scale, one might expect positive relationships between size and IT use. A majority of the articles thus use firm size as a control variable (e.g., Ang & Straub, 1998; Son & Benbasat, 2007), providing mixed results.Of the ones finding positive results, Ang and Straub (1998) found a significant and according to them ‘unsurprising’ relationship between bank size and IT outsourcing.
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indirectly related to its size. Salge et al. (2015) discuss a number of articles that found a positive relationship between size and IS investment in the context of hospitals. Dewan et al. (1998) note a number of empirical articles relating firm size to IT investments and finding positive results as well. Without referring to empirical evidence, Mithas et al. (2013) consider that since larger firms possess more slack resources, are more inclined to reach scale economies and cope with risk more easily, they are more likely to invest in IT.
Speaking of contrary findings, Son and Benbasat (2007) found that small firms were more proactive in initially adopting B2B e-marketplaces. However, as expected, larger firms seemed to be more committed in the long run, due to their excessive resources. Furthermore, Mendelson and Pillai (1998) researched whether firm size had an influence on the relationship between IT investment and clock speed, finding no significant effect. Ravichandran and Liu (2011) their results even suggest that small firms invest more intensively than large firms. Despite these mixed results, one might generally assume firm size to influence the extent to which firms invest in IT actively. Therefore, the following proposition has been formulated:
P2e: The larger the firm’s size, the more likely a firm will invest, implement, adopt, and use IT.
Industry type.
The final important organizational characteristic influencing the IT decision process is considered the industry type. Kobelsky et al. (2008) illustrate this by stating that "competitive dynamics, business processes, and the nature of the installed IT assets necessary to meet the resulting information processing requirements vary across industries" (p. 963). The statements and findings from the studied articles confirm the influence of variance in industry settings.Kobelsky et al. (2008) compared the industries that use IT to automate or transform with industries that use IT for informational objectives, concluding that automate industries generally have higher IT budgets than informate industries do. Their findings also show that, as expected, high-tech firms generally have significantly higher IT-budgets than other firms (Kobelsky et al., 2008). This matches the earlier findings of Mendelson and Pillai (1998), arguing that the IT industry typically has a higher clockspeed and thus a higher rate of change towards technology development than other industries. Karimi et al. (1996) found that IT investments were
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As previously mentioned, there are also varieties between the public and private sector mainly due to cultural constraints. This generally causes the public sector to be less innovative and risk-averse (Dawson et al., 2016; Moe et al., 2017).
Moreover, Ravichandran and Liu (2011) tend to explain the variance of IT use in
industries through the difference in information intensity. According to them, firms in information-intensive industries tend to invest more in IT in order to promote product quality and process efficiency and use it as a tool to collaborate with partners within the value chain (Ravichandran & Liu, 2011).
Lastly, there are also scholars stating that if firms are active in multiple businesses or industries, there is a growing need for information processing and, thus, IT (Dewan et al., 1998; Mithas et al., 2013). Dewan et al. (1998) refer to this scope of the firm as either unrelated diversification, related diversification, or vertical integration. The study of Xue et al. (2017) confirms the notion of diversification and shows that secondary business areas are generally characterized by more risk and less managerial control, causing them to be more experimental with IT.
The findings endorse that the likeliness of IT investment and the IT decision-making process as a whole differs from the nature of the industry. Therefore, we suggest that:
P2f: Some industry types are more likely to invest, implement, adopt, and use IT than other industry types.
4.3. Technological Factors
The third theme identified to be influencing decisions to invest, implement, adopt, and use IT is the technological domain. Accordingly, scholars generally emphasize the impact of IT capabilities and current IT infrastructures. Prior (successful) experience with IT will motivate organizations to invest more, aligning with the quotation in Huber’s article (1981) of BTOF: “when an organization discovers a solution to a problem by searching in a particular way, it will be more likely to search in that way in future problems of the same type (Cyert & March, 1963)” (p. 8).
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facilitate and predict IT implementation success (Templeton et al., 2002) and promote the decision to carry-out IT ‘in-house’ (Hall & Liedtka, 2005).
Research shows that an increasing number of employees possess IT capabilities due to developing technologies in-house, enabling them to adjust the technologies themselves
(Leonardi, 2011). Baker and Singh (2019) support this by stating that there has been a shift in the workforce, meaning that the employees become more tech-savvy and more aware of technologies than ever before, causing them to be sources of emergent IT initiatives. There are even so-called digital-native firms of whom digital blueprints are ingrained in their core
competencies (Anand et al., 2020). According to Hall and Liedtka (2005), firms that carry out IT ‘in-house’ maintain a better understanding of their technology and IT future compared to IT outsourcing. They argue that firms that consider their IT competencies not to be unique, should not produce them internally and thus decide to outsource their IT (Hall & Liedtka, 2005).
However, as the study of Ravichandran and Liu (2011) notices, there are still less IT
experienced firms experimenting with new IT applications in order to develop IT capabilities. A lack of IT capabilities could still be hindering the process of outsourcing as well, as firms are not always able to recognize their own information system requirements (Moe et al., 2017).
Another solution in case firms do not possess unique IT competencies is deciding to hire experts or consultants (Yap et al., 1994; Sillince & Mouakket, 1998; Wang et al., 2006). The study of Wang et al. (2006) showed that, because of the tacit knowledge behind ERP-systems, the influence of consultants was critical in lowering any knowledge barriers.
Son and Benbasat (2007) seem to be the only authors finding conflicting results
regarding the IT capabilities’ importance. They used IT capabilities as a control variable since a positive relationship between IT capabilities and adoption intention and participation level towards a B2B e-marketplace was expected due to previous studies. However, the findings were controversial, as IT capabilities did not have strong effects on both adoption intention and participation level (Son & Benbasat, 2007). Despite this exceptional controversial finding, the above reasoning from the other findings suggests that:
P3a: The more developed and experienced IT capabilities, the more likely a firm will invest, implement, adopt, and use IT.
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generally correlated with a firm’s underlying IT infrastructure. Renkema (2003) has a similar view, stating that the infrastructure is seen as a base foundation on which future IT components are based. Therefore, investments were often initiated due to an underlying IT infrastructure that was conceived as unsuccessful in solving information needs. Therefore, in many of the cases of the study of Renkema (2003), proposed IT investments were often justified by addressing issues in the current IT infrastructure.
As Karimi et al. (1996) describe, the underlying IT infrastructure “can enhance or limit a firm's strategic moves by providing fast response, interorganizational coordination, and
organizational flexibility, which are considered extremely important under uncertain
environmental conditions (Davenport & Linder, 1994)" (p. 62). This might differ significantly among firms, as illustrated by the studies of Mendelson and Pillai (1998) and Sillince and Mouakket (1998). Mendelson and Pillai (1998) show that the IT-industry uses a variety of internal and external communication technologies due to the clock speed of its environment, whereas the study of Sillince and Mouakket (1998) showcases that organizations such as the university belonging to their case study, did not have good IT systems, staff or technologies. In the case of these universities, developing a rich IT infrastructure was not needed to survive (Sillince & Mouakket, 1998).
Some articles show how firms use their current IT components in order to examine needs for future IT investments, such as decision-support-systems (Huber, 1981), business intelligence to detect weak signals (Rouibah & Ould-ali, 2002) and project appraisal
methodologies (Serafeimidis & Smithson, 2003). Of course, the emergence of business intelligence and analytics (BI&A) systems enable rich possibilities for organizations as well (Anand et al., 2020). As Anand et al. (2020) describe, BI&A systems are built on technologies like big data, machine learning, and Artificial Intelligence (AI), and improve data analyzing and subsequently decision-making. Following this reasoning, Constantiou and Kallinikos (2015) describe big data as an extension of business data analytics, looking at the impact big data has on the extent to which organizations are able to assess and analyze their internal and external environments. In order for firms to use big data analytics, their existing IT infrastructure might be outdated and many IT components and routines might therefore need to be replaced. There are distinct methods to collect, generate, and utilize data needed (Constantiou & Kallinikos, 2015). However, in the end, it will presumably lead to more insights and consequently, improve decision-making of managers (Anand et al, 2020).
