A Systematic Review into the Evolution of Innovation Research in
Information Systems Literature
Master thesis, MSc BA Change Management
University of Groningen, Faculty of Economics and Business
June 22nd, 2015 Gerben T. Douma Eeldersingel 44a 9726AS Groningen gerbendouma@outlook.com S2585227 Supervisors:
dr. I. Maris-de Bresser & dr. U.Y. Eseryel Co-assessor:
dr. B.J.M. Emans
Abstract
Introduction
As the environment becomes increasingly turbulent, organizations have to respond faster to the changing environment (Teece, 2007). Therefore, the capability to innovate is regarded as one of the most important sources of competitive advantage, some call the capability to innovate the main determinant of firm performance (Mone et al., 1998; Teece, 2007). In the past decades, research on innovation in the IS research field has grown so quickly that the history of the field has been ignored in the research field (Hirschheim & Klein, 2012). Recently, Forbes magazine published an article named “Top-down is dead” (Caldbeck, 2003). The author is claiming that the days of relying solely on internal R&D teams to drive innovation are over and that the concept of innovation is changing (Caldbeck, 2003). Consumer companies shift their innovation process from exclusively internal towards a more external process. They start to open up their innovation process by using their external network (Gassmann,, Enkel & Chesbrough, 2010). Think about it, how can 10, 50 or a 100 people, sitting in a Research & Development (R&D) department, out innovate millions of consumers who connect via web platforms (Caldbeck, 2003). There was a time when a technological innovation represented a product that was purely internally developed and manufactured by a single firm, before it was shipped to the consumers (Van der Ven, 2005). However, this image is changing.
The intent of this literature review is to get a theoretical understanding of the evolution of the innovation process within the information systems (IS) research field. This is a fast growing field with many different sub-communities working on specialist topics (Hirschheim & Klein, 2010). Research within the IS field is scattered across different levels of analysis and concepts.
better understanding of the current state knowledge on innovation within IS literature. This research differentiates from previously conducted reviews by synthesizing the collected data in the IS field, by looking at the nature, actors and the locus of the most used concepts in innovation research. The paper will end by discussing the evolution of the innovation process.
There are many different definitions of innovation that are used in the innovation literature (Zaltman, 1973 ;Damanpour, 1991; Evans, 1991; Davenport, 1991; Boer and During, 2001; Anderson, 2004; Crossan & Apaydin, 2010). The broad use of the term ‘innovation’ was one aspect that was obvious in the literature. Every definition emphasizes different topics of innovation like change (Evans, 1991) or creativity (Damanpour, 1991). According to Rogers (1998), innovation can involve both, the creation of entirely new knowledge as well as the diffusion of existing knowledge. This can be explained by the concept of exploration/ exploitation (Zi-Lin & Poh-Kam, 2004). Exploration is defined as firm behaviors characterized by search, discovery, experimentation and risk taking (Zi-lin & Poh-Kam, 2014). Furthermore, exploitation implies firm behavior characterized by refinement, implementation, efficiency, production and selection (Zi-lin & Poh-Kam, 2014). In order to match up with its broad scope, this review utilizes the innovation definition of Crossan & Apaydin (2010): “production or adoption, assimilation, and exploitation of a
value-added novelty in economic and social spheres; renewal and enlargement of products, services and markets; development of new methods of production; and establishment of new management systems. It is both a process and an outcome.” ( p.1155). This definition
captures a number of essential aspects of innovation. First, the definition includes both, internally produced innovations as well as the externally developed and therefore, adopted innovation. Moreover, innovation is seen as more than an explorative and creative process, it also includes the exploitation of innovation. Furthermore, it defines innovation as something that has a value-added aspect on different levels of analysis. To conclude, it focuses on the roles of innovation as a process as well as innovation as an outcome.
In order to fully understand the complete field of innovation, and the concepts within, this research initially reviewed all the aspects of innovation. In a later stage, this wide scope has been narrowed down by synthesizing the results into a comprehensive framework. The goal of this review is to answer the following question: What is the current state of the IS field
and how have the approaches to innovation evolved throughout the years?
determine their research gaps. Moreover, the discussion will help researchers get a deeper understanding of shifts within the process of innovation.
This review will begin by describing the research methodology, followed by a descriptive analysis in the result section. Subsequently, an overview of the findings will be presented and synthesized into a framework. To finalize, the findings will be discussed and implications for further research will be proposed.
Methodology
This section describes the methodology that is followed in conducting the systematic literature review. According to Tranfield, Denyer & Smart (2003) traditional literature reviews frequently lack thoroughness. Consequently, they lack means for making sense of what the body of literature is saying. These reviews can be biased by researchers and often lack rigor (Tranfield et al.,2003). In order to prevent these problems, this research makes use of systematic steps for conducting a literature review, as proposed by Tranfield et al. (2003). The steps can be seen in Table 1. In order to give a clear insight into the activities that are conducted, the remainder of this section will consist of an operationalization of the proposed steps.
Stage Step
1: Planning 1: Development of a research protocol
2: Execution 2: Identification of research
3: Selection of studies
4: Inter-rater reliability check 5: Data extraction and monitoring 6: Data synthesis
3: Reporting 7: Report and recommendations
Table 1: Steps systematic review. Adapted from Tranfield et al. (2003)
Planning
Development of the research protocol
bias (Tranfield et al., 2003). The protocol consists of two rounds of coding which are used as a guide for reviewing the literature within the scope of this review.
In the first round of coding, each of the articles that is included in the initial consideration set, which consists of 2744 articles, is reviewed (Table 2). The coding of the consideration set is done in collaboration with my colleague. We both coded approximately 50% of the consideration set. During this review all the articles that are making a direct contribution to the innovation literature are coded as relevant. After an article is notified as relevant, it is reviewed with the help of the coding scheme (Appendix A). By scanning the article for information, like level of analysis or sample size, the scheme is filled in as far as possible. The coding scheme is grounded in earlier systematic reviews and papers on how to conduct a systematic review in the IS context (Crowston, Wei, Howison & Higgins, 2012; Crossan & Apaydin, 2010; Webster & Watson, 2002). All relevant articles are coded on the following dimensions: ‘article code’, ‘author(s)’, ‘publication year’, ‘title’, ‘publication journal’, ‘level of analysis’, ‘paper type’, ‘data collection method’, ‘sample size’, ‘process or outcome’, ‘dimension of innovation’, ‘main construct’, ‘innovation terminology’ and ‘main result’ (see Appendix A for complete coding scheme). The main construct is coded by using the Inputs-Mediators-Outputs-Inputs model (IMOI) as proposed by Crowston,(2012). This model is translated to the coding scheme by adding the coding dimensions ‘input’, ‘mediator’, ‘moderator’ and ‘output’. Furthermore, a column named ‘remarks’ is utilized to administer emerging themes and information which is relevant to later data synthesis (Tranfield et al., 2003).
Figure 1:IMOI Model. Adapted from Crowston (2012)
results from this concept matrix can be seen in Appendix B. In the ‘data synthesis’ paragraph, the concept matrix will be elaborated in further detail.
Execution
Identification of research
This research is conducted in the IS literature field. More specifically, in the eight top journals in IS literature, also known as ‘AIS basket of eight’. The basket of eight contains the following journals: ‘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 IS’ and ‘MIS Quarterly’.
The selection of studies
The literature is subtracted from ‘EBSCO Host’ as a primary search engine. However, this search engine did not cover all journals for the entire time-frame. Therefore, ‘Palgrave MacMillan’, ‘AISEL’, ‘Wiley’ and ‘ScienceDirect’ are used to attain the additional literature. The search is limited to a time-frame of 20 years. Therefore, this research only contains papers from the period between January 1st 1995 – December 31st 2014. The key words that are used to search for literature are ‘Innovation’, ‘Innovative’ and ‘Innovate’. Moreover, the search is classified to ‘all text’ and the presence of only one of the key words is sufficient for the article to be included in the initial consideration set. Table 2 displays the distribution of the 2744 articles that compile the initial consideration set, with quantities and search engines used per journal.
Journal Number of publications
Search engines used European Journal of Information Systems 501 Palgrave Macmillan Information Systems Journal
215 Wiley Online Library (18), EBSCO Host (197) Information Systems
Research
Journal of Information Technology 166 EBSCO Host Journal of Management Information Systems 513 EBSCO Host Journal of Strategic Information Systems 333 Science Direct Journal of the Association for Information Systems 255 EBSCO Host (244), AISEL (11)
MIS Quarterly 543 EBSCO Host
Total ‘AIS Basket of eight’
2744
Table 2. Distribution of papers among the journals
Inter-rater reliability
To increase internal validity, my colleague and I conducted an inter-rater reliability check, by independently coding an identical set of 50 articles before the start of the 1st coding round. In two of the cases (4%), the researchers judged the relevance of the article differently. These results are above the threshold of 90%, which is the minimal acceptable score (Miles & Huberman, 1994). Internal validity is increased further by putting cases of doubt under a second (peer) review, when necessary followed by a discussion. Finally, in the second round of coding my colleague and I reassessed each other’s coding of relevant articles and adjusted errors. Moreover, 21 additional articles were excluded from the ‘relevant in first review’ set.
In order to resolve differences in viewpoints, assigned codes were compared. The differences in coding emerged mostly from a lack of clarity on the coding scheme. During an iterative process, the coding scheme has been refined. Furthermore, my colleague and I added additional codes which provided better content clarity of the papers. A detailed overview of the coding dimensions can be found in Appendix A. With the refined coding scheme and the corresponding viewpoints, the data extraction and monitoring process can be initiated.
By carefully extracting and monitoring the data, it becomes easier to do the data synthesis. The extraction of the data is done by downloading all the papers and subsequently mapping them by journal. These maps consist of sub maps, named ‘Non-relevant’, ‘Relevant’, ‘Literature Review’ and ‘In doubt’. In these maps the articles are labeled by ‘Article code_Name 1st author_Year_Abbreviation of Title’.
After mapping the papers, the articles can be coded. During this process the articles are scanned on relevance. An article is assessed as relevant, when it contains one of the terms ‘innovation’, ‘innovative(ness)’ or ‘innovate’ that is directly related to the contribution that the article is attempting to make. In order to find the terms in the article, ‘innov’ is entered into the in-document search function. The matches that appear are explored to the extent that can be assessed whether the terms contribute to the main theme or are just mentioned. As soon as an article was deemed ‘Relevant’ it is coded by following the coding scheme as described in Step 1. Moreover, an article deemed ‘Non Relevant’ is only coded by article code, authors, year and the reason why it is not relevant (see Appendix A for details). The result of this primary coding process is a complete dataset which forms the basis for the data synthesis.
Data synthesis
Data synthesis helps to derive insights from the reviewed literature. According to Tranfield et al. (2003) synthesizing is essential in reaching a higher analytic goal and to enhance the generalizability of the results. During the data synthesis, patterns and overarching themes in the dataset will become visible. Pattern finding can be very productive when the dataset is substantial (Miles and Huberman, 1994). As mentioned before, this research uses a concept matrix as described by Webster & Watson (2002) to derive patterns from the dataset. This makes it easier to decide whether a concept should be included in the scope of the review (Webster & Watson, 2002). The concept matrix provides an overview of the main concepts used and the levels of analysis of the relevant research articles (see Appendix B). This information is used for synthesizing the data into a framework. Although the concept matrix helps to give guidance, pattern finding is an iterative process, whereby the literature has to be scanned over and over again in order to make sense of it. Nevertheless, the process eventually leads to the exposure of patterns from the data.
search, discovery, experimentation and risk taking (Zi-Lin & Poh-Kam, 2004), while innovation as a process refers to the activities that firms undertake in order to develop innovations (Gupta et al., 2007). Moreover, innovation as an outcome of the innovation process is considered in terms of new ideas or processes, products and procedures (Gupta et al., 2007), while exploitation implies firm behaviors characterized by refinement, implementation, efficiency, production and selection of these new ideas (Zi-Lin & Poh-Kam, 2004). Based on the previous definitions, this article argues that the authors are referring to the same phenomenon. Moreover, process as a form of innovation is easily confused with innovation as a process (Crossan & Apaydin, 2012). Therefore, this review article will use the terms exploration/ exploitation, instead of process/ outcome
The second pattern emerged from the coding on levels of analysis. In the coding process, it became clear that innovation can be done by different actors, this led to the distinction between the actors of innovation. Within data, there is an observable division between innovation done by individuals, groups and external networks.
The third pattern found in the data is the distinction between research which either considers innovation as an open process, involving the environment, or innovation as a solely internal, closed process. Therefore, the data is synthesized into the actors of innovation that are internal and external. The actors of innovation from an individual and group level are internal, while actors from an external network are synthesized as external.
When synthesizing a big amount of data, it can be hard to recall important insights or relations within the data that have been found earlier in the process. Cobin & Strauss (2008) proposed to use memos in order to make sense of the data during the synthesizing process. When scanning relevant articles, memos are used to interpret and store information about, for example, connections between concepts, analytical ideas, limitations or further research suggestions. A standard format is used for writing the memo’s (see Appendix C for memo format). These memo’s form the basis from which the report is written.
Reporting
Report and recommendations
Results
This section will provide descriptive characteristics of the articles that are coded as relevant after the first coding round. Moreover, it will provide an overview of the current state of innovation research in the IS literature.
Figure 2. Quantity of articles on innovation per year throughout the time-frame
Descriptive analysis
Figure 2 gives an overview of the number of articles that have been published between 1995 and 2014. The graph shows an increase in the number of articles on innovation up to 2007. After 2007 the line becomes relatively stable until 2014.When comparing the figure to another review on innovation (Crossan and Apaydin, 2010), a similar increase can be seen in the period between 1995 until 2007. Moreover, in the graph of Crossan and Apaydin (2010) the same stagnation of growth, as in our graph, is shown in 2008. The data of Crossan & Apaydin (2010) covers innovation articles until July 2008. In this analysis we can see that the stagnation of the innovation research in the IS literature has continued to proceed after 2008.
Figure 3: Distribution of articles by research method
Figure 3 shows that the majority of the relevant articles are empirical papers. The distribution illustrates that 91% of the articles are empirical, this can be divided into theory testing (43%) and theory building (48%). Moreover, 7% of the papers are identified as theoretical and in 2% of the cases, articles cannot be categorized.
Furthermore, the relevant articles are coded by level of analysis, as shown in Figure 4. In following the categorization as proposed by Crowston et al. (2012) this review made a distinction between artifact level (3% of the relevant papers), individual level (23%), group level (5%), organizational level (53%), multilevel (7%) and the societal level (9%).
The societal level covers any level of analysis that is beyond one organization, such as inter-organizational IS (Hsu, Lin & Wang, 2014) or a cross-cultural diffusion of innovations (Dewan, Ganley & Kraemer, 2010). As shown in Figure 4, organizational level studies form the major part of this review, followed by the articles on the individual level by almost a quarter of the total. Once again this is consistent with the results of Crossan & Apaydin (2010). This points out that innovation research within the IS field has its main focus on organizational and individual level research. In the following section, the concepts of innovation research in the IS field will be elaborated in more detail.
Syntheses
In this section the data acquired from the review will be synthesized into a comprehensive framework (Figure 5). The goal of this systematic literature review is to provide an overview of the current state of the IS field and to review how the focus of the concepts within innovation research has evolved throughout the years. The current state of the IS field will be synthesized in the dimensions below. As described in the methodology these dimensions emerged naturally from patterns in the data. The results of the syntheses will be input in the discussion section, in which the evolution of the field will be discussed.
- The dimension ‘nature of innovation’ will show how concepts from the literature can be divided between exploration/ exploitation.
- The ‘actors of innovation’ dimension will show how concepts are divided between ‘individual’, ‘group’ or ‘external network’ level actors.
Figure 5: Framework
The Nature of Innovation
Exploration and Exploitation.
Innovation as exploration will always pave the way for innovation as an exploitation (Crossan & Apaydin, 2012). Whereas innovation exploration focuses on ‘how’ to innovate, innovation exploitation answers the question of ‘what’ (Crossan & Apaydin, 2012). Across the literature innovation has been studied as exploration and exploitation. Although most articles have a clear distinction, the division between exploration and exploitation can be ambiguous (Sood & Tellis, 2005). Moreover, some articles focus on both, exploration and exploitation (Messerschmidt & Hinz, 2013). Articles that emphasize on exploitation, focus mostly on the concepts adoption, diffusion and implementation. Moreover, exploration concepts, which are more scattered, focus on how innovative ideas originate and evolve within organizations (Nambisan, Agarwal & Tanniru, 1999). Exploration concepts will be elaborated in the following paragraph.
Innovation as exploration.
Exploration implies innovation processes characterized by search, discovery, experimentation and risk taking (Zi-Lin & Poh-Kam, 2004). This definition emphasizes the development of new methods of production and new products or services, it relates to the question ‘how’ (Crossan & Apaydin, 2012).
capable of changing as soon as the market changes. Moreover, when exploring for new innovations, a firm’s absorptive capacity is important. This makes a firm able to acquire and exploit important knowledge. The last exploration concept is agility, a firm’s ability to be agile makes it capable to deal with unexpected changes in the environment. Each of these concepts will be examined in more detail below.
As Crossan & Apaydin (2012) already mentioned, complete separation of exploration and exploitation is difficult. Moreover, exploitation always follows exploration. Therefore, exploration concepts have aspects that relate to exploitation and vice versa. The concepts that are summed up in Table 3 are categorized as more explorative in nature.
Concept Authors
Dynamic capabilities Wheeler (2002); Zahra & George (2002); Daniels & Wilson (2003); Hackbart & Kettinger (2004)
Absorptive capacity Srivardhana & Pawlowski (2007); Carlo, Lyytinen & Rose (2012); Joshi, Chi, Datta & Han (2010).
Agility Sambamurthy, Bharadwaj & Grover (2003);
Lyytinen & Rose (2006); Lu & Ramamurthy (2011); Oosterhout, Waarts & Hillegersberg (2006).
Table 3: Exploration concepts
Dynamic capabilities.
Teece et al. (1997) suggest that dynamic capabilities are unique to a firm, they are reflecting their individual idiosyncrasies and path-dependencies. Therefore, Teece et al. (1997) and Daniel & Wilson (2003) agree that dynamic capabilities of a firm are likely to become a sustained competitive advantage. A firm can develop dynamic capabilities by incremental business process improvement, or via radical leapfrogging strategy, depending on the aspirations and the exploitation capabilities to integrate and reconfigure competencies (Daniel & Wilson, 2003; Sambamurthy et al., 2003). Hackbarth & Kettinger (2004) investigate the difference in characteristics of firms with different aspirations for incremental or radical innovation. Absorptive capacity (APAC), which can be seen as a dynamic capability, will be elaborated subsequently.
Absorptive capacity.
Carlo, Lyytinen & Rose (2012) define APAC as the firm’s ability to identify and acquire knowledge. Absorptive capacity is presented as a separate concept, although it can be seen as part of the concept ‘dynamic capabilities’. The motive for this is based on the fact that many research articles separated this concept from dynamic capabilities and research absorptive capacity as an individual concept (Srivardhana & Pawlowski, 2007; Joshi, Chi, Datta & Han, 2010; Carlo, Lyytinen & Rose, 2012).
Joshi, Chi, Datta & Han (2010) argue that IT-enabled knowledge enhances the innovation process by facilitating the capabilities to explore new opportunities for the creation of new products and services. In order to keep exploring new innovation opportunities, a firm needs to be capable of acquiring knowledge. This makes APAC an essential capability in the process of innovation.
Moreover, the literature elaborates on the significant role of IS in enhancing a firm’s innovation capability. Joshi et al. (2010) introduce the concepts of PACAP and IT-RACAP. IS helps to enhance a firm’s knowledge acquisition capability (Joshi et al., 2010). This provides the, so called, IT-PACAP (Joshi et al., 2010). Moreover, IS helps to support knowledge transformation and exploitation, which provides the, so called, IT-RACAP (Joshi et al., 2010).
Agility.
Agility is a firm’s capability to deal with unexpected changes in the environment by successfully exploring the innovative responses that exploit changes as opportunities to grow and prosper (Oosterhout et al., 2006). This capability is vital to the innovation process and competitive performance of firms (Sambamurthy et al., 2003). Agile firms have the capability to anticipate or respond to changes in a timely manner because they are constantly exploring the environment for opportunities and threats (Oosterhout et al., 2006). By creating a certain flexibility in their organizational processes and IS, a firm becomes able to address changes with a predetermined response (Lu & Ramamurthy, 2011). Agility is relevant on two interrelated levels, namely network and enterprise level (Oosterhout et al., 2006). When the network around the firm is agile, it will force the firm to be agile as well (Oosterhout et al., 2006). Therefore, Oosterhout et al., (2006) investigated the ‘Agility gap’ between external network factors and the enablers/ disablers of business agility.
between exploration and exploitation. Therefore, this research will examine innovation as exploitation below.
Innovation as exploitation.
Exploitation can be characterized as refinement, implementation, efficiency, production and selection (Zi-Lin & Poh-Kam, 2004). Moreover, exploitation literature relates to the question ‘what’ (Crossan & Apaydin, 2012). The main concepts on exploitation can be found in Table 4.
This section examines the most researched concepts in innovation research that relate to exploitation. Namely, diffusion of innovation, innovation assimilation and the tri-core model of innovation. Diffusion of innovation relates to the process in which innovation is communicated throughout the firm, while assimilation refers to the extent to which a technology has become a routine. To conclude, the tri-core model distinguishes three types of IS innovation. These concepts will be examined in more detail below.
Concept Author
Diffusion of Innovation Bajwa, Lewis, Pervan & Lai (2005); Bunker, Kautz & Nguyen (2009); Igira (2008); Vega, Chiasson & Brown (2008); Jiang & Sarkar (2009); Cavusoglu, Hu & Li (2010); Wang (2010); Lyytinen & Rose (2003); Zhu & Kraemer (2005); Theo, Wei & Benbasat (2003); Zhu, Kraemer, Gurbaxani & Xu (2006) Innovation Assimilation Fichman (2001); Cho& Kim
(2008);
Fichman & Kemerer (1997); Teo, Wei & Benbasat (2003); Chatterjee, Grewal & Sambamurthy (2002); Liang, Saraf, Hu & Xue (2007); Wolf, Beck & Pahlke (2012)
Tri-core model of Innovation Jeyaraj, Balser, Chowa & Griggs (2009); Wolf, Beck & Pahlke (2012); Chau; Yan Tam (1997); Chatterjee, Grewal & Sambamurthy (2002); Grover (1997)
Table 4: Exploitation concepts
Diffusion of innovation.
Rogers (2003) conceptualizes diffusion as a process in which innovation is communicated through certain channels between members of a social system. Data analysis showed that most research in the diffusion literature is focused on adoption (Rai, 1995; Theo, Wei & Benbasat, 2003; Zhu, Kraemer, Gurbaxani & Xu, 2006). In recent years, IS initiatives have encountered some serious resistance from prospective users (Cavusogulu, Hu, Lit & Ma, 2010). As mentioned before, exploration and exploitation needs to be balanced. Without exploitation, the innovation process cannot be completed. Therefore, research is focused on issues with the diffusion of IS (Cavusogulu et al., 2010).
influenced by three types of actors, namely influentials, who promote innovation adoption, opponents, who inhibit adoption, and imitators, who are information seekers and are affecting the influentials and the opponents. Results show that opponents play a crucial role in determining the diffusion pathway of an innovation (Cavusogulu et al., 2010). When the diffusion pathway of an innovation is completed the innovation can be assimilated, this concept will be examined in the following section.
Assimilation of innovation.
Assimilation is defined as the extent to which the use of technology is diffused across organizational work processes and becomes routine within activities associated with these processes (Fichman & Kemerer, 1997). Fichman (2001) describes the following stages of the assimilation process. (1). Awareness, the decision makers are aware of the innovation. (2). Interest, organization is committed to learning about the innovation. (3). Evaluation/ Trial, the organization has initiated evaluation or a trial. (4). Commitment, the organization has committed to use a specific innovation. (5). Limited deployment, the organization started to use the program regular but limited. (6). General deployment, the innovation is substantially used.
Tri-core model of innovation.
Swanson (1994) proposed a typology of IS innovation, called the tri-core model. Grover, Fiedler & Teng (1997) elaborate on the paper of Swanson (1994) by empirically testing aspects of the model, while Grover (1997) proposes extensions for the model of Swanson (1997) by incorporating contingencies, like strategy and technology.
The model, which is built on the model of Daft (1978), distinguishes three fundamental types of IS innovation. Type 1 innovation is involving the IS product itself (Grover, 1997), like an open systems interface (Chau & Tam, 1997). Type 2 and Type 3 innovations extends the IS innovation in the general organizational environment as a support service (Grover, 1997). Type 2 innovations assist the administrative core of a company with, for example, financial accounting systems, payroll systems or executive information systems (Grover, 1997; Swanson, 1994). Type 3 innovations integrate IS into the core work processes and external relationships of the firm. Type 3 innovations enhance the products or services which are using IS. Examples of Type 3 innovations are web technologies (Chatterjee et al, 2002), business-to-consumer (Jeyaraj, Balser, Chowa & Griggs, 2009), grid-based architectures (Wolf et al, 2012) or other inter-organizational systems (Grover, 1997).
The tri-core model is an extensively researched concept in the IS field. Most research articles containing the tri-core model, use the model in order to define the technology that they investigate (Chatterjee et al, 2002; Chau & Tam, 1997; Wolf et al, 2012; Jeyaraj et al, 2009). The types of innovations that the tri-core model distinguishes relate to the question of ‘what’ (Crossan & Apaydin, 2012). Therefore, this concept is listed as an exploitation concept.
The actors of innovation.
The actors of innovations are the people who are involved in the innovation. This paragraph will examine innovation concepts which have an individual, group and external network level of analysis. Moreover, these actors are synthesized into an internal and external actors of innovation.
This section will first elaborate on the concepts with a closed locus of innovation, that investigates innovation from an individual perspective, followed by the concepts from a group level of analysis. To conclude this section, concepts from the external network perspective will be further elaborated.
Internal
Innovation from an individual level.
What emerged from the data is that most articles on an individual level of analysis can be divided into articles about decision making and articles about people’s ability to innovate. This section examines the most researched concepts on the individual level of analysis (Appendix B). First the concept of personal innovativeness in information technology (PITT) is examined. PITT refers to the willingness of an individual to use new technology (Lu, 2005). This is followed by the concept of computer self-efficacy which relates to a the judgment of a person’s own capabilities to make use of a computer (Marakas, Yi & Johnson, 1998). To conclude this section will examine the CEO and CIO decision making process. These concepts will be examined in more detail below.
Concept Authors
Personal innovativeness in IT (PITT) Elie-dit-cosaque, Pallud & Kalika (2011); Thatcher & Perrewé (2001); (Bock, Zmud, Kim & Lee (2005); Lu; Yao; Yu (2005); Gray, Parise & Lyer (2011); Featherman, Valacich & Wells (2006).
Computer self-efficacy (CSE) / Computer anxiety (CA)
Mourmant, Gallivan & Kalika (2009); Argwal, Sambamurthy & Stair (2000); Thatcher & Perrewe (2002).
CEO/ CIO decision making Boonstra (2003); Chatterjee et al. (2001); Sharma & Rai (2014); Liang et al. (2007). Table 5: Individual perspective concepts
Personal innovativeness.
IS. The results of Lu et al. (2005) proof that individual perceptions of usefulness and ease of use are significantly endorsed by PITT. Which contributes to an overall positive outlook towards new IS. This is essential for the adoption of innovation from an individual level. Therefore, PITT is essential to the innovation process.
Featherman (2006) theorizes that, when a consumer has high levels of PITT he or she will evaluate e-service as non-authentic and less artificial which, in turn, has a positive relation on the perceived risk. High levels of PITT will give employees perceived behavior control over the technology (Cosaque, Pallud & Kalika, 2011). Therefore, PITT will lower the employees computer anxiety (Cosaque, 2011; Thatcher & Perrewé, 2002) and positively influences the computer self-efficacy (Thatcher & Perrewé, 2002).
Computer Self-Efficacy/ Computer Anxiety.
Computer self-efficacy (CSE) refers to an individual’s judgment of their capabilities to use computers in diverse situations (Marakas, Yi & Johnson, 1998). Individuals who have a high computer CSE are more likely to form a positive perception on IS. Furthermore, CSE is correlated with computer anxiety (CA) (Thatcher & Perrewé, 2002). CA refers to fear for computer usage, such as the loss of important data or the fear of mistakes (Thatcher & Perrewé, 2002). Moreover, individuals who believe that they have high levels of PITT judge themselves as having more CSE (Argwal, Sambamurthy & Stair, 2000) and lower levels of CA (Crosaque et al., 2011). These individuals are more likely to adopt new innovations, which makes this concept an important aspect of the innovation process.
CIO/ CEO theories/ articles.
assimilation by meeting the terms of institutional pressures. These results have important implications for the theoretical understanding of factors that are of importance for IS leader’s decision making. Moreover, these decisions can have a big impact on the group level, for example, R&D investment decisions.
Innovation from a group level.
What emerged from the data on the group level of analysis is that most research on group level within innovation research is about the R&D department. R&D will be elaborated more extensively in the following section.
Concepts Authors
R&D Kleis, Chwelos, Ramireze & Cockburn
(2012); Xue, Ray & Sambamurthy (2012); Barhan, Krishnan & Lin (2013); Banker, Wattal, Plehn-dujowich (2011)
Table 6: Group level concepts
Research and development.
External
Innovation from an external network.
This section describes the most researched concepts from an external network perspective, which emerged from the concept matrix (Appendix B). First, the concept of open innovation will be examined. Open innovation refers to the use of outside knowledge in the innovation process (Chesbrough, 2006). Furthermore, the concept of open source software (OSS) development will be elaborated. This concept refers to software that is open source. Therefore, its source code can be altered by everybody, which gives the opportunity of collaborative development. In the remainder of the synthesis section, these concepts will each be examined in further detail.
Concept Authors
Open innovation (Crowdsourcing, Co-creation)
Schlagwein & Anderson (2013); Thoren, Agerfalk & Edenius (2014); Feller, Finnegan, Hayes & O'Reilly (2012); Leimeister, Huber, Bretschneider & Krcmar (2009); Feller, Finnegan & Nilsson (2011); Ceccagnoli, Forman, Huang & Wu (2012); Chen, Marsden, Zhang (2012); Fuller, Muhlbacher, Matzler & Jawecki (2009)
Open source software (OSS) development
Morgan, Feller & Finnegan (2013); Marcedie & Mijinyawa (2011); Zhang, Hahn, & De (2013); Eseryel (2014)
Table 7: External network perspective concepts
Open Innovation.
external sources of innovation. According to Leimeister et al. (2009), customers are one of the biggest resources of innovation. Chesbrough (2006) defines open innovation as “the use of
purposive inflows and outflows of knowledge to accelerate internal innovation and expand the markets for external use of innovation (p. 1)”. Since recently, researchers have conducted a
lot of research on the open-innovation concept. Croudsourcing is a form of open innovation that is enabled by IS (Schlagwein & Andersen, 2014). This can consist of, for example, an idea generation contest for consumers or other stakeholders (Leimeister et al., 2009). Leimeister et al. (2009) did research on the influence of incentives and activation-supporting components on the participation and motivation of an individual to compete in such an idea generation contest. Moreover, Schlagwien & Andersen (2014) examine IT-enabled organizational leaning in relation with crowdsourcing on which they propose the so called ‘ambient organizational learning’ framework. Feller et al. (2012) tries to orchestrate sustainable crowdsourcing by looking at the knowledge mobility, appropriability and stability processes, which they argue to be necessary for successful crowdsourcing. Moreover, Feller et al. (2011) investigate how open innovation can possibly transform governments, while Thoren (2014) explores how newspaper organizations can cope with the concept of open innovation.
Co-creation can be regarded as a subdivision of open-innovation. Co-creation is the creation of value by working together with stakeholders, which are typically customers or end users (Chen et al., 2012). Chen et al. (2012) demonstrate the influence of peer feedback and the sponsor company response speed on the duration and the contribution of active participants. Moreover, Fuller et al. (2009) argue that the design of the interaction tool determines the perceived empowerment and experienced enjoyment of consumers which, in turn, has an impact on the willingness to participate. Furthermore, Ceccagnoli et al. (2012) investigate how co-creation between firms is associated with an increase in performance and how this performance improvement is affected by ownership of intellectual property and downstream capabilities.
Open source software.
Open innovation has been closely linked to open source software (OSS) development (Eseryel, 2014). OSS development teams are open innovation teams which use their own resources to create novel software code and share this code for free (Von Hippel & Krogh, 2003). Recent research on OSS reveals how a firm can create and capture value by accessing an external value network of complementors (Morgan, Feller & Finnegan, 2013).
(2013) examine OSS from a participant level, by investigating the joint influence of an external community response, the roles of members of the external continuity and their participation in an OSS development project.
Discussion
As described in the introduction, innovation is regarded as one of the most important sources of competitive advantage (Mone et al., 1998; Teece, 2007). When looking at the popularity of the innovation topic, it can be concluded that researchers are aware of the increasing importance of innovation. Nevertheless, in accordance with other research (Grover, 1997; Crossan & Apaydin, 2012) this review shows that IS innovation research is isolated and fragmented.
While innovation research in general is scattered, some aspects can be generalized across the IS literature field. What can be observed, for example, is that research with an exploratory nature is more often characterized by its open locus of innovation, while research with an exploitative nature is in general exclusively internal. Furthermore, the nature of the research concepts on an individual level can often be characterized as exploitative, while research concepts from a group or external network level have an explorative nature. It can be argued that successful exploitation is more dependent on a person’s inherent characteristics, like CA or CSE, while exploration is more successful when conducted in groups or external networks. Moreover, it can be concluded that the literature on exploration is much more scattered across different concepts in comparison with exploitation. This is perhaps a consequence of the more individual/ cognitive character of exploratory research. Furthermore, a shift within the actors of innovation and the locus of innovation can be seen from the data. The actors of innovation shift from the group level (R&D), towards innovation from the external network. Moreover, the locus of innovation evolves from an internal towards a more external locus of innovation. The former will be elaborated in the ‘actors of innovation’ paragraph and the later in the ‘locus of innovation’ paragraph of this discussion. This change in the nature of knowledge creation could be due to the development of IS (Eseryel, 2014) or due to the increasingly dynamic environment (Beer, Voelpel, Leibold & Tekie, 2005).
making the following contributions. The first contribution of this review is the overview on the current state of the IS field. The second contribution is an overview of the main concepts used in innovation research with an exploration and an exploitation focus. Third, this research reviews the process of innovation by elaborating on the actors of innovation. The fourth and final contribution of this paper is the examination of internally and externally focuses innovation concepts. Below the review will be discussed in an attempt to answer the following questions.
- Is there an preference in the literature on exploration or exploitation and can an observable move be seen from one to the other?
- Is there an observable shift on the actors involved in the innovation process?
- Is there a observable move from an internal towards an external locus of innovation?
The Nature of Innovation
From the data set it became clear that there is a distinction in the literature between exploration and exploitation. Although, most articles have a clear distinction, the division between exploration and exploitation can sometimes be ambiguous (Sood & Tellis, 2005). Some articles focus on both aspects (Messerschmidt & Hinz, 2013) whereas other articles focus neither on exploration nor on exploitation (Melas & Zampetakis, 2014). Many reviews emphasize on the adoption, diffusion and implementation of IS (Wu & Lederer, 2009), while there is limited attention paid to how innovative ideas originate and evolve in organizations (Nambisan, Agarwal & Tanniru, 1999). As can be seen in Appendix B, the most used concepts in exploration literature are ‘Dynamic capabilities’, ‘Absorptive capacity’ and ‘Agility’. These concepts all focus on a firm’s ability to cope with environmental dynamism and uncertainties. Without these capabilities a firm loses the ability to change. Firms need these competencies in order to stay flexible and explore new opportunities.
of everyday processes throughout the firm, while the Tri-core model makes a distinction between three types of innovation.
Although exploration research is much more scattered than exploitation research, this review leads to the conclusion within IS literature that there is no clear preference in an explorative or exploitative nature of innovation. Moreover, the distinction between exploration and exploitation can be ambiguous at times. Exploitation always follows exploration in the innovation process (Crossan & Apaydin, 2012). The transition from exploration to exploitation could perhaps be the cause of this ambiguity.
Actors of Innovation
As can be seen in the framework (Figure 5), the actors of innovation are divided into individual, group and external network level. What stands out is that the papers which are focused on an individual level of analysis investigate people inherent factors, which relate to a person’s ability to innovate, like CA and PITT. Moreover, there are some articles that focus on decision making (Chatterjee et al., 2001 ;Boonstra, 2003). When looking at research for a group level of analysis, R&D stands out as the main innovation driver. Furthermore, the papers that focus on the external network perspective mostly investigate how the environment of the organization can best be involved in the innovation process.
What stood out from research on a group level of analysis, is that firms with a high degree of diversification are more likely to innovate trough acquisition than through R&D (Banker et al., 2011). Due to the fast changing dynamic environment it is more effective and often cheaper to acquire a company from the external environment. The acquired company often has highly specialized skills and knowledge which can bring value to the buying firm, that is hard to create internally. Firms will increasingly look outside the borders of the firm to acquire the knowledge necessary to keep innovating in a fast paced dynamic environment. This is probably the reason for the shift in actor of innovating from group towards external network. The locus of innovation will be discussed further in the following section.
Locus of Innovation
co-creation. From the data analysis, it can be concluded that there has been a move from an internal towards an external locus of innovation. Especially, users are increasingly integrated in the innovation process which makes a firm able to understand customer requirements and to integrate user’s knowledge (von Hippel, 1986). It can be argued that this is the cause of the fast development of IS (Eseryel, 2014). Recent developments made firms able to communicate with a big community and to analyze big amounts of data, which makes it more appealing for firms to use their network. Open innovation makes the firm able to acquire knowledge from a much bigger pool, when compared to solely internal innovation. However, there are also negative sides to open innovation. According to Enkel, Kausch & Gassmann (2005), there is a risk of loss of know-how, over dependence on customers, potential of innovations are mostly incremental and a potential for misunderstanding within the network. Nevertheless, every concept has its limitations and the era of open innovation has just begun Therefore, there are still many aspects that can be researched in the future.
Limitations
Future Research
Due to the fast development of IS, further research in the innovation area will continue to shift more and more towards external networks with an open perspective on innovation. Open innovation is still a young concept. Therefore, open innovation has some underdeveloped elements that can be further elaborated in future innovation research. For example ‘upstream innovation’ is a popular research field, while research on the ‘downstream’ side has been less intensively researched (Grassmann et al., 2010). Nevertheless, a lot of valuable knowledge can be found downstream, which can have a significantly strong impact on innovation (Grassmann et al., 2010). Therefore, future studies should be more focused on downstream innovation coming from the supplier side of the value chain. Moreover, open innovation is defined as “The use of purposive inflows and outflows of knowledge to accelerate internal
innovation and expand the markets for external use of innovation” (Chesbrough, 2006, p.1).
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