Information technology and search for innovation: New findings from healthcare
and other sectors
Alexander Hensen (S1861034)
Faculty of Economics and Business, University of Groningen Duisenberg Building, Nettelbosje 2
9747 AE Groningen, The Netherlands Supervisor: Dr. J.Q. Dong Co-assessor: Dr. J.D. van der Bij
Date: June 19, 2017
Word count: 15297 (including references and tables: 4242)
ABSTRACT
The advance of information technology (IT) increasingly change the ways through which
organizations innovate into a more open approach to access external knowledge. In the healthcare sector, search for knowledge using IT systems is especially important, because healthcare is characterized by knowledge-intensive, professional work. To improve knowledge search, however, simply investing in IT is not enough; organizations need to develop absorptive capacity to effectively learn and benefit from external knowledge for innovation. There is little knowledge about how IT may enable organizations’ absorptive capacity as most studies have mainly focused on the direct
relationship between IT and innovation performance. This study takes a hierarchical benefit perspective to investigate this question using a two-stage model by distinguishing the first-order benefits (e.g., knowledge and structure benefits) and second-order benefits (i.e., innovation
performance). Instead of generally looking at IT investment and use, we further open up the black box of IT by theorizing specific IT-enabled search that is relevant for explicit knowledge and tacit
knowledge. By using a large-scale longitudinal dataset from Germany in 2003-2007, we find in both manufacturing and service organizations that innovation performance increases only by IT-enabled search through knowledge benefits, rather than structure benefits. Additionally, we found that the results differ for organizations in the healthcare sector and other sectors. Our findings shed light on the IT-enabled absorptive capacity literature by providing a comprehensive understanding of the
mechanisms through which IT-enabled search increases innovation performance.
Keywords: information technology, IT-enabled search, IT-enabled absorptive capacity, health information technology, innovation
1. INTRODUCTION
In recent years, there has been a change in how organizations innovate, because knowledge plays an increasingly central role in the creation of innovation opportunities and competitive outcomes (Dong and Yang, 2015; Dong and Netten, 2017; Grant, 1996; Nonaka, 1994; Nonaka and Takeuchi, 1995).
While organizations focused heavily on their own resources decades ago, nowadays organizations are seeking external knowledge sources and adopting strategically more open approaches to access knowledge for potential innovation opportunities in their external environment (Chesbrough et al., 2006). Knowledge search is essential for effective knowledge-intensive, professional work, such as in healthcare organizations. Moreover, the healthcare industry is particularly an important sector for all developed economies (Wilkinson, 2011) and has therefore the potential to develop innovations.
Information technology (IT) helps organizations to get access to external knowledge across
organizations’ boundaries, which makes it a key source for innovation (Cohen and Levinthal, 1990;
Kleis et al., 2012; Tambe et al., 2012) and for new product development (Barczak et al., 2007; Barczak et al., 2008; Durmusoglu et al., 2006; Durmuşoğlu and Barczak, 2011; Nambisan, 2003). Also in the digitalization trend in healthcare, IT plays an important role (Boonstra and Van Offenbeek, 2010;
Fichman et al., 2011; Hajli, 2014; Ker et al., 2014) to search for knowledge and partners for innovation. To effectively learn and benefit from external knowledge sources, studies show that organizations not only have to invest in IT, but also have to develop their absorptive capacity (Dong and Yang, 2015; Joshi et al., 2010; Roberts et al., 2012). Cohen and Levinthal (1990) introduced the absorptive capacity concept and defined it as a firm’s ability to recognize the economic value in external knowledge, assimilate it, and utilize it for commercial ends. Scholars have later
conceptualized absorptive capacity as a different externally oriented organizational learning process (Dong and Yang, 2015; Lane and Lubatkin, 1998; Lewin et al., 2011; Tsai, 2001) and as a dynamic knowledge processing capability that enhances firm innovation (Zahra and George, 2002).
There is an increasing interest in how IT enhances absorptive capacity (e.g., Dong and Yang, 2015;
Joshi et al., 2010; Roberts et al., 2012), facilitates search processes during organizational learning, and ultimately increases innovation (Bardhan et al., 2013; Dong and Netten, 2017; Kleis et al., 2012;
Saldanha et al., 2017; Tambe et al., 2012). Within the healthcare industry, studies have shown that IT can improve the performance of healthcare organizations (Ayal and Seidman, 2009; Devaraj and Kohli, 2003; Kohli et al., 2012; Menon and Kohli, 2013), while there are also mixed findings among healthcare organizations regarding the impact of IT on innovation (Aral and Weill, 2007; Boonstra, 2011). Interestingly, the function of IT remains often an exogenous black box (e.g., Dong and Netten, 2017; Kleis et al., 2012) and scholars have mostly focused on the direct effect of IT on innovation (Banker et al., 2006; Kleis et al., 2012). However, very few literature explores the detailed functions of IT systems affecting organizations’ absorptive capacity. Additionally, existing literature has focused
on the direct relationship between IT and innovation, but the mechanisms underlying the relationship between IT and innovation remain, until now, unclear. Prior literature has only focused on the direct relationship between IT and innovation, but not on the mechanisms underlying the relationship. Some scholars, however, did already show that IT directly affects knowledge management (Alavi and Leidner, 2001) and changes in organizational structure (Attaran, 2004), but these scholars, in turn, failed to show whether IT ultimately affects innovation performance.
Furthermore, functions of IT systems and their effects regarding an organization’s absorptive capacity are still unclear. Accordingly, easy accessible IT investment data is generally used as a measure, but offers little insight into the IT capabilities that an investment may have achieved (Burke and
Menachemi, 2004). In the innovation process, searching for knowledge is one of the first critical activities (Laursen and Salter, 2006). Until recently, Dong and Netten (2017) revealed that IT investment is associated with organizations’ external search breadth and depth. IT contributes to this process through improving knowledge acquisition and assimilation by supporting information exchanges and sharing among organizations (Dong and Yang, 2015; Rai et al., 2006; Saraf et al., 2007). This IT-enabled search process can provide an organization with two types of knowledge:
explicit and tacit knowledge (Nonaka, 1994). Both are critical for innovation, because they
dynamically interact with each other in creative projects. Moreover, interaction between individuals is important in the innovation process (Leonard and Sensiper, 1998). Consequently, communication is important for knowledge search and takes place through either direct or indirect interaction with partners (Chesbrough et al., 2006; Laursen and Salter, 2006). Therefore, the current study proposes that organizations have the potential to directly obtain explicit knowledge through IT-enabled information search on the one hand, and indirectly obtain tacit knowledge from social interactions through IT-enabled relation search on the other hand. We define IT-enabled information search as the organizational learning from codified knowledge using IT systems. IT-enabled relation search refers to organizational learning from tacit knowledge from innovative partners using IT systems.
This current study attempts to fill the gaps by illustrating how IT improves organizations’ search activities and thus how IT has the potential to enhance organizations’ absorptive capacity through IT- enabled information search and IT-enabled relation search. Next, we propose a two-stage model, first- and second-order benefits of IT-enabled search. In the first stage of our approach, by drawing on the absorptive capacity literature, we aim to explore the effect of IT-enabled information and relation search on knowledge benefits (i.e., the level of improvement of knowledge management and product design process) and structure benefits (i.e., the level of improvement of reorganization of work processes and relationships with other organizations). In the second stage of our approach, we investigate whether these first-order benefits affect innovation performance. Since there is no prior study in literature that simultaneously investigates the increased absorptive capacity of an organization
through IT-enabled search and the emerging first- and second-order benefits, this study provides a more holistic picture of all of the concepts.
To test our model and hypotheses, we use a large-scale longitudinal data set from the German Mannheim Innovation Panel (MIP) database in 2003-2007, which covers both manufacturing and service sectors. The empirical findings reveal that both IT-enabled information search and IT-enabled relation search are positively related to knowledge benefits. The findings further show that only IT- enabled relation search is positively related to structure benefits. Finally, only knowledge benefits, and not structure benefits, are positively related to innovation performance.
This study makes three important contributions to the literature. First, since both tacit and explicit knowledge are important in an initial step in innovation, we propose two IT-enabled search processes.
By drawing on the absorptive capacity literature, this study theorizes an organization’s use of IT systems in information search to access explicit knowledge and relation search to access tacit
knowledge for innovation. Second, we provide a comprehensive understanding of the direct effects of IT-enabled information and relation search and whether these effects result in improved innovation performance. We draw on a two-stage model (Barua et al., 1995; Mukhopadhyay and Kekre, 2002), theorizing IT-enabled search in direct, first-order and indirect, second-order benefits. Third, the empirical results show that IT-enabled search increases innovation performance through knowledge benefits and not through structure benefits. A Sobel test confirmed the findings by proving a
significant mediation path from IT-enabled search to innovation performance only through knowledge benefits.
The rest of the paper is organized as follows. In the next section, we introduce the theoretical
background and relevant literature on IT-enabled absorptive capacity. Then, hypotheses are developed.
Subsequently, data and measures are reported, followed by presenting the results. Finally, the empirical findings and conceptual model are discussed, as well as the implications for theory and practice, and the limitation and directions for future research.
2. THEORETICAL BACKGROUND
2.1. Externally oriented organizational learning
Several decades ago, scholars established the foundations in research on organizational learning (Cangelosi and Dill, 1965; Cyert and March, 1963). Consequently, literature regarding this theory has increasingly accumulated over the last couple of years. Organizational learning, at very basic level, is the process by which an organization assimilates novel knowledge and insights and exploits internal knowledge (Slater and Narver, 1995). Through ‘learning alliances’ organizations can increase their capability development and decrease technological uncertainties by acquiring information and
exploiting knowledge created by other organizations (Grant and Baden-Fuller, 1995). Organizational memory plays a major role in organizational learning and refers to the ‘amount of stored information or experience an organization has about a particular phenomenon’ (Moorman and Miner, 1997).
Organizational memory can both serve as an important base for change through generative learning processes and help organizations search for the relevant type of information and the way it is analyzed (Slater and Narver, 1995). There are two types of organizational memory (March, 1991; Moorman and Miner, 1998). The first one is declarative memory, which refers to knowledge about facts and events (know-what). The other type of organizational memory is procedural memory, which contains knowledge about routines, processes, and procedures (know-how). Decades earlier, explicit and tacit knowledge were distinguished in literature (Polanyi, 1967). Explicit knowledge is characterized by simple transmission of formal language, clear expression and knowledge that is easily codified and readily transferable into digital format. The tacit component refers to knowledge that is difficult to communicate in formal language, but may be inferred from actions and therefore requires a rich communication medium.
This study focusses on the degree of interaction between organizations, which is essentially externally oriented organizational learning (Argote and Miron-Spektor, 2011; Vera and Crossan, 2004). Past studies propose that externally oriented organizational learning occurs in the own environment of the focal firm, as well as in the external environment (e.g., Crossan et al., 1999). Consequently, there are two types of externally oriented organizational learning processes: there is either direct interaction or indirect interaction between organizations (Chesbrough et al., 2006; Laursen and Salter, 2006).
Generally, direct interaction refers to the explicit knowledge which is directly transferrable among individuals and organizations. Contrastingly, in indirect interaction, tacit knowledge is often embedded in social context and thereby requires to establish relationships beforehand. Namely, relationships with partners enable individuals of organizations to retrieve and select tacit knowledge indirectly from social interactions.
Organizational knowledge transfer depends on how easily the underlying knowledge sources can be communicated, interpreted, and acquired (Kogut and Zander, 1992). In terms of the organizational learning theory, information in these two types of externally oriented organizational learning is gathered from two sources in the information acquisition process, as well as related to two types of organizational memory (Slater and Narver, 1995). On the one hand, in an organization’s information search process, knowledge from direct interaction is gathered from individuals, stored and used in individuals’ declarative memory (know-what). On the other hand, regarding an organization’s relation search, in order to e.g., learn from other organizations’ experience and thus acquire tacit knowledge, information comes from social interactions extracted from procedural memory (know-how). This tacit
‘how and why’ knowledge is enclosed in an organization’s social context and is more unique than articulable and observable ‘what’ knowledge (Spender, 1996). This makes search for relations
important, because tacit knowledge is characterized by high complexity and transferring this information requires face-to-face interactions (Huber, 1991).
Past literature proposed that absorptive capacity is a new way to understand the learning and innovation carried out by organizations (Cohen and Levinthal, 1990). Explaining an organization’s interaction using externally oriented organizational learning can be emphasized through the absorptive capacity concept, which extends the insights from the individual level to the organizational level (Cohen and Levinthal, 1990). Cohen and Levinthal (1990) define absorptive capacity as ‘the firm’s ability to recognize the economic value in external knowledge, assimilate this knowledge, and apply it to commercial ends’. Thus, absorptive capacity determines to which degree organizations appropriate value and how they retrieve benefits from external knowledge stocks (Schilling, 2005; Tsai, 2001). A similarity between the absorptive capacity and organizational learning theory is the reliance on a process oriented perspective. Additionally, the absorptive capacity theory is conceptualized by the literature as a different externally oriented organizational learning process (Lane and Lubatkin, 1998) and as a dynamic capability of processing knowledge that enhances firm innovation (Zahra and George, 2002).
2.2. IT-enabled absorptive capacity
There is an increasing interest in how IT can play a facilitating role in search processes during organizational learning. Roberts et al. (2012) suggest that it is important to deepen our understanding of the mechanisms through which IT enables absorptive capacity in their systematic literature review.
Dong and Yang (2015) showed that IT-enabled absorptive capacity leads to more effective
organizational learning processes and ultimately to improved organizational outcomes. Next, it was shown that innovation outcomes are affected by different types of IT-enabled knowledge capabilities in their sample of 110 firms (Joshi et al., 2010).
Zahra and George (2002) introduced distinctive capabilities in the absorptive capacity, such as knowledge acquisition and assimilation. Knowledge acquisition refers to an organization’s capability to identify and obtain knowledge that is critical to their operations (Zahra and George, 2002). More effective acquisition of new knowledge and improved use of existing knowledge is also a key aspect of acquisition (Inkpen and Dinur, 1998). The ability to acquire and select essential knowledge is likely to be enhanced by using IT systems (Tippins and Sohi, 2003). It was also shown that IT is one of the critical factors influencing the performance of knowledge transfer across different firms (Frank et al., 2015). Additionally, it was suggested that information and communication technologies help to facilitate knowledge sharing among individuals (Song et al., 2007). Examples of IT systems
supporting knowledge acquisition are the use of online search engines, IT-enabled scientific literature and patent database systems, and IT-enabled technology portals. Examples of IT-enabled relation
search to ultimately acquire tacit knowledge are the use of central homepages of scientific institutions and technology transfer departments for establishing relationships.
Knowledge assimilation is defined as an organization’s routines and processes that allow it to interpret, process, analyze, and understand the knowledge obtained from external sources. It was shown that firms can enhance their assimilation of external knowledge using IT by developing organizational memory in the form of electronic repositories (Alavi and Leidner, 2001).
Organizational learning can also be enhanced by the use of IT when firms accumulate valuable external information that is saved digitally and retained for future projects (Tippins and Sohi, 2003).
In sum, prior literature suggests IT can enhance the absorptive capacity among organizational members and partners. IT contributes to the improvement of acquisition and assimilation of external knowledge by supporting information exchanges and sharing among firms (Rai et al., 2006; Saraf et al., 2007). New knowledge is generated through the combination and dynamic interaction of both tacit and explicit knowledge (Nonaka and Takeuchi, 1995). Explicit knowledge is easier transferrable using IT among individuals, because it is codified, while tacit knowledge, such as skills and routines, require social interactions due to the stickiness of this type of knowledge. Additionally, since tacit knowledge is often embedded in social context, only rich media communication tools are suitable to transfer this type of knowledge. Consequently, knowledge portals may be better used for sharing explicit
knowledge, but ICT may be better suited for sharing tacit knowledge (Goodman and Darr, 1998).
Therefore, searching for partners and establishing contacts is very important to acquire both explicit and tacit knowledge required for innovation. Since IT can facilitate externally oriented organizational learning and both direct interaction and indirect interaction are important factors in organizational learning processes, organizations focus on respectively IT-enabled information search for explicit knowledge and IT-enabled partner search for tacit knowledge. In the current study, IT-enabled information search is defined as organizational learning from codified knowledge using IT systems.
IT-enabled relation search is related to organizational learning from tacit knowledge from innovative partners using IT systems.
2.3. First-order and second-order benefits
In past studies, IT is seen as an exogenous black box (e.g., Joshi et al., 2010). Traditionally, IT investment is measured by value of installed IT capital stock and quantity of IT hardware in organizations (Bardhan et al., 2013; Dong and Yang, 2015; Kleis et al., 2012). IT investment as a measure has been used in previous research, because the widespread availability of investment data.
However, IT investment measures provide little insight into the IT capabilities that the organization’s investment may have achieved (Burke and Menachemi, 2004). Instead, to gain more insight into the effect of IT, the use of IT has the potential to create direct, first-order and indirect, second-order benefits, drawing upon a two-stage model (Barua et al., 1995; Mukhopadhyay and Kekre, 2002). On
the one hand, first-order benefits refer to organizations’ actions and can be influenced directly by organizations. On the other hand, second-order benefits are competitive outcomes and include the effect of external factors such as environmental changes and other organizations’ actions that are unable to control by the focal organization.
In the current study, IT-enabled information search and IT-enabled relation search are proposed to have a direct effect on knowledge benefits and structure benefits, and thus creating first-order benefits.
Knowledge benefits are defined as the level of improvement of knowledge management and product design process. Knowledge management consists of several processes such as generation, storage, transformation, application, and embedding of organizational knowledge (Alavi and Leidner, 2001;
adapted from Hedlund, 1994). For example, an increase in knowledge benefits arises from the use of IT in the process of acquiring external knowledge through improving communication (Barua et al., 2004; Malhotra et al., 2005) and better integration (Rai et al., 2006; Saraf et al., 2007). Structure benefits are defined as the level of improvement of reorganization of work processes and relationships with other organizations. An increase in structure benefits arises from the opportunity to acquire novel knowledge through establishing relationships using IT (Rai and Tang, 2010; Rai et al., 2012).
Reorganization changes an organization’s use of routines and communication networks, which creates a new environment for learning and the potential for both internal and external resources to be used in new combinations leading to innovation (Kogut and Zander, 1992; Schumpeter, 1934). Innovation performance reflects as the second-order benefits, as it is influenced by both knowledge benefits and structure benefits. Innovation performance is defined as the commercial outcome of new products and services.
3. HYPOTHESES
3.1. IT-enabled search and knowledge benefits
As a result of IT-enabled absorptive capacity, explicit knowledge can be obtained through the search by using IT systems. Accordingly, codified scientific documents can be communicated and shared, which improves knowledge management and design of new products (i.e., knowledge benefits). We propose that IT-enabled information search is positively related to knowledge benefits for two reasons.
First, information search supported by IT systems can enhance an organization’s acquisition of explicit knowledge (Tippins and Sohi, 2003). In the process of IT-enabled search for codified information, knowledge acquisition is enhanced, because explicit knowledge is codified, stored in a hierarchy of databases and is accessed with high quality, reliability, and timely information retrieval systems (Smith, 2001). Accordingly, in an interorganizational network, the use of IT has been shown to be an effective communication and information sharing tool between partners (Dong and Yang, 2015; Dong and Netten, 2017). In highly important scientific codified texts and documents, IT has increased the
sharing rate and improved the access to electronic versions of journals and public research databases (Kremp and Mairesse, 2004). Additionally, IT facilitates much more rapid collection of explicit knowledge than the rate of knowledge collection without the use of IT and therefore supports explicit knowledge acquisition (Roberts, 2000). IT can facilitate access to explicit knowledge by lowering the costs of accessing shared knowledge in an interorganizational network (Cui et al., 2012; Dong and Yang, 2015). Moreover, IT has been found to eliminate barriers to communication among
organizations’ departments by integrating fragmented flows of knowledge (Gold et al., 2001).
Second, IT systems can also enable an organization’s assimilation of explicit knowledge (Alavi and Leidner, 2001). Knowledge assimilation supports interpretation and understanding of newly acquired explicit knowledge, because external knowledge is often hard to internalize (Leonard-Barton, 1996).
Thus, IT systems supporting to search for explicit knowledge have been shown to enable the internalization and interpretation of explicit knowledge, which ultimately leads to improved knowledge benefits.
For example, knowledge portals and electronic patent databases have the potential to increase knowledge management and product design processes. Individuals can easily obtain explicit
knowledge (e.g., by digitally accessing this information about technologies). Due to the use of these knowledge portals and databases, organizations increase their knowledge acquisition, because, e.g., there is a low barrier for accessing the portals and databases. Moreover, organizations increase also their knowledge assimilation, as this explicit knowledge is codified and can therefore be processed and internalized easier. In sum, information search supported by IT systems have the potential to enable absorptive capacity and improve knowledge management and product design processes. Thus, it is hypothesized that:
H1: IT-enabled information search is positively related to knowledge benefits.
Next, in searching for tacit knowledge from innovative partners, IT has the potential to enable absorptive capacity. Tacit knowledge is important in innovations, because this type of knowledge embedded in social contexts is harder to access for competitors due to its stickiness (Du Plessis, 2007).
Organizations search for tacit knowledge by using central homepages of scientific institutions and technology departments for establishing contacts, which ultimately results in improved knowledge management and product design process (i.e., knowledge benefits). We propose that IT-enabled relation search is positively related to knowledge benefits because of two reasons.
First, IT-enabled relation search has the potential to enhance the acquisition of tacit knowledge. The use of IT systems is not limited to the transfer of either explicit or tacit knowledge, but it fosters all modes of knowledge transfer (Raven and Prasser, 1996; Riggins and Rhee, 1999; Scott, 1998). As mentioned before, there is an enhanced acquisition of tacit knowledge, because IT lowers the barrier to
access shared knowledge in an interorganizational network (Cui et al., 2012; Dong and Yang, 2015).
Cohen and Leventhal (1990) stated that the ability to know where tacit knowledge is located is crucial.
Namely, next to the capability to obtain knowledge, acquisition also includes the capability to identify knowledge. Since tacit knowledge is often embedded in interactions among individuals and in social contexts, it is important to identify where this tacit knowledge is located in an interorganizational network. The use of homepages of scientific institutions and technology transfer departments for establishing relationships with other organizations contributes to the awareness of other’s capabilities (i.e. tacit knowledge embedded in social contexts) and acquisition of tacit knowledge (Cohen and Levinthal, 1990). Therefore, IT-enabled relation search enhances the acquisition of tacit knowledge.
Second, IT-enabled relation search has the potential to enhance the assimilation of tacit knowledge.
IT-enabled relation search can enhance the routines and processes that allow organizations to interpret, analyze, understand, and internalize externally obtained tacit knowledge (i.e., knowledge
assimilation). After relationships with other organizations has been established as a result of using central homepages of scientific institutions and technology transfer departments, tacit knowledge can be shared among organizations. Zahra and George (2002) stated that larger experiential learning, which requires interpreting, analyzing, understanding and internalizing of tacit knowledge, results from greater interaction with external knowledge sources. Ultimately, IT-enabled relation search has the potential to increase knowledge benefits, because organizations possess improved assimilation of tacit knowledge.
An example of how IT-enabled relation search is likely to increase knowledge benefits can be drawn from organizations’ use of central homepages of scientific institutes and knowledge management systems. Organizations can obtain tacit knowledge of e.g., how to organize knowledge and improve product design processes by obtaining tacit knowledge from partners and relationships. To establish relationships with organizations to acquire tacit knowledge, organizations can use central homepages of scientific institutes and technology transfer departments to identify which potential partners can provide them with the needed tacit knowledge. Through IT-enabled relation search, organizations enhance their tacit knowledge acquisition, because they know where to access the tacit knowledge embedded in relationships. Moreover, since organizations are able to internalize and process the tacit knowledge from social interactions in relationships, also the assimilation of tacit knowledge is enhanced. Taken together, while organizations engage in relation search, the use of IT systems facilitates this search for tacit knowledge and enhance the absorptive capacity and allow organizations to improve their knowledge management and product design processes (i.e., knowledge benefits).
Thus, it is hypothesized that:
H2: IT-enabled relation search is positively related to knowledge benefits.
3.2. IT-enabled search and structure benefits
Due to IT-enabled absorptive capacity, explicit knowledge can be obtained through IT-enabled information search. The use of knowledge portals enables organizations to communicate and share explicit knowledge, which improves reorganization of work processes and relationships with other organizations (i.e. structure benefits). It is proposed that IT-enabled information search is positively related to structure benefits for two reasons.
First, information search supported by IT systems has the potential to enhance an organization’s acquisition of explicit knowledge. IT-enabled information search is characterized by the search for explicit, codified knowledge. As mentioned before, the acquisition of explicit knowledge is enhanced, because explicit knowledge is accessed with ease because codification allows information to be stored and accessed with high quality, reliability, and timely information retrieval systems (Smith, 2001).
Moreover, IT has been shown to be an effective knowledge sharing tool between partners (Dong and Yang, 2015) and increases the knowledge sharing and accessing rate of scientific literature and database systems (Kremp and Mairesse, 2004). More specifically, IT can support an organization in identifying and obtaining explicit knowledge (i.e., acquisition of knowledge) about different
approaches how to manage work processes (Attaran, 2004). Accordingly, an enhanced acquisition of explicit knowledge through IT-enabled information search has the potential to improve the
reorganization of work processes and relationships with other organizations.
Second, IT can enhance an organization’s assimilation of explicit knowledge. As individuals in organizations search for information supported by IT systems, they are likely to use e.g., knowledge transfer portals. Searching for information using digital knowledge portals allows an organization to improve interpretation and understanding the explicit knowledge. More specifically, interpretation, analyzing, understanding, and internalizing explicit knowledge (i.e., assimilation) often occurs when novel knowledge reveals that a different work process or method is chosen over a previous process or method (Riggins and Rhee, 1999). During IT-enabled information search, knowledge portals such as scientific literature and patent database systems have been found suitable for explicit knowledge assimilation (de Carvalho, 2001). In short, IT systems support organizations to understand and internalize explicit knowledge and thus enhancing knowledge assimilation. An improved assimilation of explicit knowledge through knowledge portals will potentially result in the improvement of reorganization of work processes and relationships with other organizations (i.e., structure benefits).
An example of IT-enabled information search resulting in improved structure benefits is the use of knowledge portals and patent database systems. By e.g., searching and obtaining digital drawings and technical documentations, these IT-enabled codified knowledge search processes allow organizations to obtain and internalize explicit knowledge (i.e., acquisition and assimilation) from
interorganizational networks that eventually become second nature to a focal organization (Riggins
and Rhee, 1999). Consequently, IT-enabled search for explicit knowledge enhances an organization’s absorptive capacity, which contributes to the reorganization of work processes and relationships with other organizations (i.e., structure benefits). Thus, it is hypothesized that:
H3: IT-enabled information search is positively related to structure benefits.
Next, due to IT-enabled absorptive capacity, organizations are able to obtain tacit knowledge through the search by using IT systems. Consequently, organizations involved in IT-enabled search for tacit knowledge obtained from relationships are able, through learning and an enhanced absorptive capacity, to improve their work processes and relationships with others (i.e., structure benefits). We propose that IT-enabled relation search is positively related to structure benefits because of two reasons.
First, IT-enabled relation search enhances an organization’s acquisition of tacit knowledge. IT systems enable an organization to identify and obtain tacit knowledge (i.e., acquisition of knowledge) through the use of central homepages of scientific institutions and technology transfer departments for establishing contacts. Searching for partners using IT systems can improve the use of existing knowledge and supports the identification of new tacit knowledge (Inkpen and Dinur, 1998). As selecting and identifying relevant tacit knowledge is an important aspect of acquisition, relationships established through IT-enabled relation search give individuals the ability to retrieve and select tacit knowledge from social contexts. In large interorganizational networks for sharing knowledge,
organizations understand each other’s information requirements and are able to obtain and identify the needed information (Malhotra et al., 2005), supporting the acquisition of tacit knowledge. In addition, the relational view relies on the fact that organizations are motivated to even share more information with other organizations when there is an increased dependency (e.g., when an organization relies on a smaller number of suppliers) (Dyer and Singh, 1998). Partners are more willing to make relationship- specific investments, and build stronger interfirm capabilities. Thus, establishing contacts as a result of IT-enabled relation search increases the acquisition of tacit knowledge.
Second, IT-enabled relation search can also enable an organization’s assimilation of tacit knowledge;
learning from tacit knowledge obtained using IT systems contribute to an organization’s ability to interpret, analyze, understand, and internalize the tacit knowledge. It has been found that
organizations, while they search for and obtain tacit knowledge using IT systems, are likely to understand and share their views of the same work situations and processes either in the same or in a different light (Bhatt, 2001), thus enhancing an organization’s tacit knowledge assimilation.
Additionally, social integration mechanisms are structures that build organizations’ social capital (Zahra and George, 2002), stimulating connectedness, interaction, coordination, and communication among organizational members by developing networks of individuals and knowledge. IT can
facilitate the development of more efficient processes for managing alliances, such as when a partner- specific alliance sets up an online mechanism for accessing knowledge, sharing, and internalizing of knowledge (Ding et al., 2010; Dong and Yang, 2015; Malhotra et al., 2007). Similarly, with regard to organizational learning, IT allows organizations to access previously stored information in the organizational memory of both their own as well as other organizations’ knowledge bases, which create operational efficiencies (Hult et al., 2004). Consequently, IT systems can enhance the interpretation, understanding, and internalization of tacit knowledge (i.e., knowledge assimilation), which eventually contribute to the improvement of reorganization of work processes and relationships with other organizations (i.e., structure benefits).
An example of IT-enabled relation search increasing structure benefits is an IT system which is called
“knowledge map system”. Similar to access homepages of scientific institutions and technology transfer departments, these IT systems support individuals in organizations to point to people who own information, thereby creating opportunities for knowledge exchange and increasing an organization’s knowledge acquisition. These knowledge maps provide tacit knowledge exchange because there is an increase in chance of personal meeting and establishment of relationships with other organizations (Terra, 2000). Consequently, these IT systems lead to an increase in face-to-face contacts that encourage shared experiences and learning by doing and observation, which improves e.g., the reorganization of work processes and thereby also improving the understanding and internalizing of knowledge (i.e., knowledge assimilation). In short, IT systems can enhance the absorptive capacity of organizations during IT-enabled relation search, which eventually contribute to structure benefits.
Thus, it is hypothesized that:
H4: IT-enabled relation search is positively related to structure benefits.
3.3. Knowledge and structure benefits and innovation
Knowledge is a crucial component for the basis of competition (Grant, 1996), and especially tacit knowledge can be a source of advantage because of its inimitability and non-substitutability. However, the simple acquisition and assimilation of knowledge does not automatically guarantee that an
organization has a strategic advantage (Zack, 2002). Knowledge management is suggested to be an antecedent of innovation (Nonaka, 1994; Nonaka and Takeuchi, 1995). Consequently, knowledge management has the potential to contribute to improved organization outcomes (Darroch and
McNaughton, 2002): e.g., recombining new knowledge with existing knowledge was associated with high innovations (Sabherwal and Sabherwal, 2005). Additionally, an organization that effectively manages knowledge is also likely to be involved in organizational learning (Sinkula et al., 1997). To effectively meet the needs of the market for organizations’ their new products, organizations should possess both technological and market knowledge (Fleming, 2002; Utterback, 1994). This is also
consistent with research suggesting that new products and innovations will be more successful when an organization retains appropriate stocks of technological and market experience (Nerkar and Roberts, 2004). On the one hand, organizations have the potential to obtain technological knowledge from IT-enabled information search in technology portals and scientific databases. On the other hand, organizations broaden and deepen their market knowledge from their network ties (McEvily and Zaheer, 1999), supported by organizations’ IT-enabled relation search. Both technological and market knowledge result in second-order benefits via improved first-order knowledge benefits. Therefore, improved knowledge benefits from IT-enabled information and relation search contribute to the improvement of the share of total sales from new products and services through the use of both technological and market knowledge. Thus, it is hypothesized that:
H5: Knowledge benefits is positively related to innovation performance.
First-order structure benefits, such as improvement of reorganization of work processes and relationships with other organizations, arise through the use of IT-enabled search and increase
innovation performance eventually. The exchange of specialized, relationship-specific assets are likely to create more value than non-specialized, generic assets (Subramani, 2004) and contribute to an interorganizational competitive advantage (Dyer and Singh, 1998). Additionally, literature supports evidence that IT deployments in supply chains result in closer buyer-supplier relationships
(Subramani, 2004). In these relationships, organizations learn and develop a better interorganizational understanding of the potential needs and opportunities among partners in the supply-chain.
Accordingly, organizations recognize that their competitive advantage is derived from knowledge resources embedded in social relationships with others (Uzzi and Lancaster, 2003). Not only organizations are better able to recognize and understand others’ needs, also the extent of resource exchange is affected by social interaction and trust, which in turn affects product innovation (Figueroa and Conceicao, 2000; Tsai and Ghoshal, 1998). It is important to realize what the needs and
opportunities of partners are and exchange resources in order to successfully innovate and develop new products and services. In short, organizations which adapt their structure to promote collaboration with partners (i.e., structure benefits) are able to achieve improved innovation performances. Thus, it is hypothesized that:
H6: Structure benefits is positively related to innovation performance.
3.4. IT and innovation in healthcare
Since the healthcare sector is characterized by knowledge-intensive and professional work, IT-enabled search has a different effect on knowledge and structure benefits in healthcare organizations than organizations in other sectors. Consequently, knowledge and structure benefits have different effects on innovation performance in healthcare organizations than organizations in other industries. We
argue that the impact of IT-enabled search on first- and second-order benefits will differ for organizations within the healthcare sector and other sectors for two reasons. First, the use of IT enables the absorption of great portions of digital data (Joshi et al., 2010; Kleis et al., 2012), which is especially important in knowledge-intensive sectors, such as in healthcare organizations. Both explicit and tacit knowledge can be used to improve knowledge management, reorganization of work
processes, and relationships with other organizations (i.e., first-order benefits). Second, it has been shown that IT resources are important knowledge sharing tools to facilitate processes between organizations (Dong and Yang, 2015; Kleis et al., 2012). IT systems can improve interorganizational work processes and enable integration between partners in a network. Especially in the healthcare sector, there is a high need for integration between different departments and organizations (Minkman et al., 2007). To ensure and contribute to the quality of care, partners in the care chain or care network function as a whole due to integration (Ouwens et al., 2005). Consequently, we argue that the quality of care contributes to the commercialization of new medical products and services (i.e., innovation performance).
For example, healthcare organizations have the potential to use IT systems to search for medical knowledge, such as specific diagnostic- and treatment-related information or work routines of partners in the healthcare supply chain. Consequently, the search for explicit and tacit knowledge has the potential to result in first-order structure benefits (e.g., improvement of knowledge management or reorganization of work processes). Better knowledge management has the potential to result in increased commercialization of healthcare-related products and services. Additionally, better alignment through reorganization and establishing relationships with other organizations in the healthcare supply chain is important, as it lead to more integrative and efficient processes for patients (Djellal and Gallouj, 2005). Subsequently, organizations profiting from their structure benefits have the potential to succeed in their innovations. Thus, it is hypothesized that:
H7: The impact of IT-enabled search on first- and second-order benefits will differ for organizations in the healthcare sector and other sectors.
The relationships of this study are illustrated in figure 1. The conceptual model consists of seven hypotheses (H1-H7). In H1-H4, the dependent variables are knowledge and structure benefits. In H5 and H6, the dependent variable is innovation performance. In H7, the healthcare industry is a moderator.
Figure 1. Conceptual model
4. METHODS
4.1. Data
Testing the hypotheses was done based on data covering the period from 2003-2007 of the European Center for Economic Research (ZEW)’s Mannheim Innovation Panel (MIP). MIP database, introduced in 1993, is a large-scale panel data set including analyses of determinants and effects of innovation activities in German manufacturing and service organizations. This data was collected on behalf of the Federal Ministry of Education and Research (BMBF) by the ZEW. Since 1993 ZEW gathers
innovation-related data annually from organizations across all industries for policy and research purposes. For a detailed description of the survey, see Peters (2008). By conducting the MIP, Germany contributed to the European Commission’s Community Innovation Surveys (CIS) and thus provides internationally comparable data. The CIS is the primary data source for organizations’ innovation activity (K. Smith, 2005) and it has been widely used in literature (e.g., Dong and Netten, 2017;
Laursen and Salter, 2006). Germany was ranked second in the Global Innovation Index in 2007 (at the time when innovation performance data was collected in this study) with the U.S. leading on top and U.K., Japan, and France completing the top five list (Dutta and Caulkin, 2007). Consequently, the results of the current study are generalizable to other innovative countries.
The methodology of the survey and definitions of innovation indicators are strongly related to the recommendations on innovation surveys set out in the Oslo Manual (OECD and Eurostat, 1997), which contributes to the reliability of the collection and interpretation of data by providing well- grounded and established guidelines. The CIS uses the same instruments across countries; extensive pilot tests were conducted in each country, increasing the understandability, reliability, and validity of
Healthcare industry IT-enabled
information search
IT-enabled relation search
Knowledge benefits
Structure benefits
Innovation performance First-order benefits Second-order benefits
H1 H2
H3 H4
H5
H6
H7
the survey questionnaires (Ziegler and Nogareda, 2009). The MIP has been used in past research (e.g., Dong and Netten, 2017; Horbach, 2008; Ziegler and Nogareda, 2009), however this study uses a new portion of data and is more recent up to 2007.
To construct the sample, data was obtained from the MIP database with the criteria that IT-enabled information search, IT-enabled relation search, knowledge benefits, structure benefits, and innovation performance are available. Since the questions about IT-enabled search were only included in the year 2003, this data was included in the sample. Data for knowledge and structure benefits (first-order benefits) was only available in the year 2005. Questions about innovation performance (second-order benefits) were available for every year from 2001 till 2011. Data for innovation performance in 2007 was included and was linked to the data for IT-enabled search in 2003 and knowledge and structure benefits in 2005. All control variables IT expenditure, R&D expenditure, organization size,
organization age, M&A, and location were available in the year 2003 and therefore included in the sample. A time lag between 2003, 2005, and 2007 can avoid reverse causality (Podsakoff et al., 2003).
In the end, the constructed sample comprised 1028 organization-year observations with some missing values in several variables. Table 1 provides a summary of the sample distribution across service industries based on NACE Rev. 1 codes. The healthcare industry is limited to organizations with a NACE Rev. 1 code “33”, including organizations within the industry of manufacturing medical, precision and optical instruments.
Table 1. Industry distribution of the sample
NACE Rev. 1 code
Industry Obs. Percentage (%)
10-14 Mining and quarrying 17 1.7
15, 16 Manufacture of food products and tobacco 32 3.1
17-19 Textiles, clothing and leather products 30 2.9
20-22 Wood, paper and printing 43 4.2
23, 24 Refining petroleum, code manufacture, and chemical products 31 3.0
25 Manufacture of rubber and plastic products 43 4.2
26 Glass, ceramics, other non-metallic mineral products 28 2.7
27, 28 Manufacture of basic metals and fabricated metal products 94 9.1 29 Manufacture of machinery, weapons and ammunition, and domestic appliances 67 6.5 30-32 Manufacture of office machinery and computers, electrical machinery, and
television and communication equipment
39 3.8
33 Manufacture of medical, precision and optical instruments 51 5.0
34, 35 Manufacture of motor vehicles, other transport equipment, and aircraft and
spacecraft 17 1.7
36 Manufacture of furniture, jewelry, musical instruments, sports equipment, games and toys
21 2.0
51 Wholesale and commission trade, except of motor vehicles and motorcycles 42 4.1 50, 52 Retail trade, and repair of motor vehicles and motorcycles 36 3.5
60-63, 641 Land transport, water transport, and air transport 92 8.9
65-67 Banking, insurance 33 3.2
72, 642 Computer related activities, IT, telecommunications 57 5.5
73, 742, 743 Scientific and technical services 107 10.4
741, 744 Business-related services 49 4.8
745-748, 90 Other services 58 5.6
70, 71 Real estate activities and renting of machinery equipment and household goods 41 4.0
Total 1028 100
4.2. Measures
4.2.1. IT-enabled information search
Organizations can benefit from IT during the process of acquiring external knowledge through improving communication (Barua et al., 2004; Malhotra et al., 2005) and better integration (Rai et al., 2006; Saraf et al., 2007). Organizations that use IT are likely to enhance their ability to acquire and retrieve useful knowledge (Tippins and Sohi, 2003). IT-enabled information search was measured by two questions. The first question for IT-enabled information search provides insights whether an organization searches information about research results using IT-enabled scientific literature or patent database system. The second question indicates whether an organization uses knowledge portals in the internet to search for information about research results. IT-enabled information search was calculated as the sum of two binary variables. A question was measured using a binary scale; a “0” means no use of IT-enabled search, while “1” means that the organization uses this method. The scale of the variable ranges from “0” till “2”, where “0” indicates no IT-enables search, “1” indicates moderate IT-enabled
search, and “2” indicates high IT-enabled search. IT-enabled information search was measured in 2003.
4.2.2. IT-enabled relation search
The use of IT is key to build a knowledge network, because it provides organizations the opportunity to acquire novel knowledge through establishing relationships (Rai and Tang, 2010; Rai et al., 2012).
IT-enabled relation search was also measured by two questions. The two questions indicate whether an organization uses a central homepage of scientific institutions and technology transfer departments for establishing contacts. IT-enabled relation search was calculated as the sum of two binary variables as well. A question was also measured using a binary scale; a “0” means no use of IT-enabled search, while “1” means that the organization uses this method. The scale of this variable is equal to the scale of IT-enabled information search. IT-enabled relation search was also measured in 2003.
4.2.3. Knowledge benefits
An organization’s use of IT allows it to improve their absorptive capacity to acquire external
knowledge (Dong and Yang, 2015; Joshi et al., 2010; Roberts et al., 2012). Knowledge benefits were measured by two questions. Knowledge benefits are by previous literature considered as the result of improved knowledge management (Pertusa-Ortega et al., 2010) and refers to the knowledge applied for new product development (Ahn et al., 2006). The questions for knowledge benefits in this study contain information about the improvement of knowledge management and/or change of product designs. Knowledge benefits were calculated as the sum of two binary variables. A question was measured using a binary scale; a “0” means no knowledge benefits, while “1” means that the
organization benefits from this method. The scale of the variable ranges from “0” till “2”, where “0”
indicates low performance, “1” indicates moderate performance, and “2” indicates high performance.
Knowledge benefits were measured in 2005.
4.2.4. Structure benefits
An organization’s organizational structure and cross-functional communication have been found to improve absorptive capacity if they lead to improved knowledge sharing among departments and individuals (Daghfous, 2004; Lane and Lubatkin, 1998). Structure benefits were measured by two questions as well. The questions comprise information whether an organization changes the internal organizational structure to cope with partners and establishes new relationships with other
organizations. Structure benefits were calculated as the sum of two binary variables as well. A question was measured using a binary scale; a “0” means no structure benefits, while “1” means that the organization benefits from this method. The scale of the variable is the same as the scale of knowledge benefits. Structure benefits were measured in 2005.
4.2.5. Innovation performance
Innovation performance was measured by the share of total sales from new products and services (Aral and Weill, 2007; Faems et al., 2003). Innovation performance measures not the number of new
products and services released in a specific period of time, but an organization’s actual sales of new products. Consequently, we provide a direct measure of success in commercializing innovation that traditional innovation measures may not capture (Leiponen and Helfat, 2010)
4.2.6. Control variables
Several factors that might influence IT-enabled search and first-order benefits are controlled. Control variables in the year 2003 are included in the sample. IT investment is the first control variable, which indicates an organization’s total expenditure in IT hardware, software, services and labor scaled by total sales (Bardhan et al., 2013; Han and Mithas, 2013; Mithas et al., 2012). Next, R&D investment was included, representing an organization’s reliance on internal knowledge source (Laursen and Salter, 2006) and its effort to develop absorptive capacity (Cohen and Levinthal, 1990). R&D investment was measured as an organization’s R&D expenditure scaled by it total sales (Shimizutani and Todo, 2008). Third, organization size is controlled by the natural logarithm of the number of total sales. Fourth, organization age is controlled by a binary variable, separating new startups within 3 years from more mature organizations (Laursen and Salter, 2006). Fifth, it was controlled for mergers and acquisitions (M&A), indicating whether an organization was involved in a M&A. Last, an organization’s geographic location was controlled, indicating whether an organization is located in West or East Germany. There are differences between regions in Germany due to historical and economic factors (Van de Vrande et al., 2011). Table 2 provides a description of the measures in this study. Table 3 shows the descriptive statistics and correlations of all variables.
Table 2. Description of measures
Description Scale References
IT-enabled information search
Information about research results using IT- enabled scientific literature or patent database systems and knowledge portals
0 = no; 1=
moderate;
2 = high
Barua et al., 2004;
Malhotra et al., 2005;
Rai et al., 2006; Saraf et al., 2007
IT-enabled
relation search Use of central homepages of scientific
institutions and technology transfer departments for establishing relationships
0 = no; 1=
moderate;
2 = high
Rai and Tang, 2010; Rai et al., 2012
Knowledge
benefits Improvement of knowledge management and
change of product designs 0 = no; 1=
moderate;
2 = high
Joshi et al., 2010; Kleis et al., 2012
Structure benefits Change internal organizational structure to cope with partners and establishes new relationships with other organizations
0 = no; 1=
moderate;
2 = high
Daghfous, 2004; Lane and Lubatkin, 1998 Innovation
performance
Share of total sales from innovative products Ratio Aral and Weill, 2007;
Faems et al., 2003 IT IT expenditure in hardware, software, services
and labor scaled by total sales Ratio R&D R&D expenditure scaled by total sales Ratio
Size The natural logarithm of total sales Continuous
Age Whether an organization was established in <3
years 0 = no; 1 =
yes M&A Whether an organization was involved in a
merger and acquisition in <3 years
0 = no; 1 = yes Location Whether an organization locates in East
Germany or West Germany 0 = West;
1 = East Industry dummy Whether an organization belongs to an industry
sector (22 industry sectors)
0 = no; 1 = yes
Table 3. Descriptive statistics and correlations
Obs. Mean SD (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
(1) Innovation performance
793 0.14 0.23 (2) Knowledge
benefits
980 0.40 0.59 0.16 (3) Structure benefits 982 0.51 0.66 0.07 0.25 (4) IT-enabled
information search
799 0.36 0.66 0.19 0.20 0.21 (5) IT-enabled relation
search
791 0.24 0.53 0.14 0.20 0.17 0.48
(6) IT 766 0.02 0.08 0.13 0.04 0.07 0.12 0.12
(7) R&D 974 0.02 0.04 0.11 0.13 0.12 0.38 0.25 0.01
(8) M&A 926 0.06 0.24 0.04 0.09 -0.02 0.05 -0.02 0.00 0.10
(9) Size 1028 2.15 1.55 0.01 0.05 0.09 0.15 0.06 0.02 0.15 0.08
(10) Age 952 0.03 0.17 0.10 0.02 -0.01 0.02 0.00 0.02 0.03 0.16 -0.03
(11) Location 1027 0.41 0.49 -0.02 0.07 -0.04 0.01 0.03 -0.01 -0.03 -0.06 -0.19 -0.08
Note: correlations in bold are significant with p < 0.05.
5. RESULTS
5.1. Hypotheses testing
Ordinary least squares (OLS) regression was used to analyze the data. Before conducting the regression analysis, the distribution of the variables was checked and it was found that they met the OLS assumptions. Multicollinearity was also examined by checking the variance inflations factors (VIFs) in the regression analysis. It was suggested that multicollinearity was not a problem in our analysis, since the results showed that the VIFs were all below the threshold of 10. A time lag design of two years with random effects model was chosen and therefore the results are not likely to be driven by reverse causality or time-invariant unobservable characteristics.
Table 4. OLS regression results for knowledge benefits
(1) (2) (3)
IT-enabled information search 0.128*** 0.093*
(0.044) (0.048)
IT-enabled relation search 0.126**
(0.051)
IT -0.014 -0.159 -0.178
(0.314) (0.344) (0.337)
R&D 0.869 0.385 0.242
(0.610) (0.702) (0.702)
M&A 0.085 0.105 0.122
(0.104) (0.113) (0.113)
Size 0.040** 0.021 0.021
(0.017) (0.019) (0.019)
Age -0.076 0.035 0.030
(0.141) (0.153) (0.150)
Location 0.087* 0.073 0.082
(0.048) (0.052) (0.052)
Industry dummies Yes Yes Yes
Constant 0.520*** 0.493** 0.030
(0.182) (0.199) (0.197)
R2 0.060 0.080 0.100
F 1.73 1.64 2.03
n 679 558 547
Note: * p < 0.1; ** p < 0.05; *** p < 0.01. Standard errors are in parentheses. Dependent variable is knowledge benefits.
Table 4 presents the regression results for knowledge benefits. First, the control model was estimated, then IT-enabled information search was added, and eventually added IT-enabled relation search. In the control model, it was found that larger organizations had increased knowledge benefits. For IT-
enabled information search, the full model shows that this type of IT-enabled search was statistically significant and positively related to knowledge benefits. Thus, H1 was supported. For IT-enabled relation search, the full model shows that this type of search was also statistically significant and positively related to knowledge benefits. Thus, H2 was supported.
Table 5. OLS regression results for structure benefits
(1) (2) (3)
IT-enabled information search 0.074 0.060
(0.048) (0.053)
IT-enabled relation search 0.112**
(0.057)
IT -0.133 -0.321 -0.328
(0.342) (0.379) (0.375)
R&D 1.407** 1.128 0.883
(0.658) (0.771) (0.781)
M&A 0.015 0.016 0.022
(0.113) (0.125) (0.126)
Size 0.074*** 0.062*** 0.064***
(0.019) (0.021) (0.021)
Age -0.130 -0.044 -0.044
(0.154) (0.169) (0.167)
Location 0.089* 0.052 0.029
(0.052) (0.058) (0.057)
Industry dummies Yes Yes Yes
Constant 0.022 0.275 0.409*
(0.203) (0.220) (0.219)
R2 0.050 0.070 0.090
F 1.45 1.57 1.80
n 682 561 549
Note: * p < 0.1; ** p < 0.05; *** p < 0.01. Standard errors are in parentheses. Dependent variable is structure benefits.
Table 5 presents the regression results for structure benefits. First, the control model was estimated, then IT-enabled information search was added, and finally IT-enabled relation search was added. In the control model, it was found that larger organizations and organizations with higher R&D investment had increased structure benefits. In the full model, there was no positive relation found between IT-enabled information search and structure benefits. Thus, H3 was not supported. However, IT-enabled relation search had a statistically significant and positive effect on structure benefits. Thus, H4 was supported.
