Business-Civil Society Collaborations in South Korea: A Multi-Stage Pattern Matching Study.
| Verfasser | Sinkovics, Noemi |
| Posten | RESEARCH ARTICLE |
1 Introduction
This paper uses an empirical example to demonstrate how a multi-stage patternmatching process can inform and substantiate the construction of partial least squares (PLS) models and the subsequent interpretation of and theorizing from the findings. We document the research process underlying our empirical investigations of business - civil society collaborations in South Korea. We first outline why and how a multiple-pattern matching process can enhance the use of partial least squares structural equation modeling (PLS-SEM). We then provide a framing for our empirical study and discuss how the study contributes to the international business and international management literature.
PLS-SEM as a method and SmartPLS as a software tool (Ringle et al. 2015) have rapidly gained popularity over the past decade. This is partially due to PLS-SEM's suitability for predicting and theorizing, and its ability to deal with complex models, estimate formative constructs, and handle smaller sample sizes, among other features (Richter, Cepeda, et al. 2016; Ringle et al. 2020). However, despite the method's high level of sophistication, its relative flexibility and the user-friendliness of the software have generated some unintended consequences (Zeng et al. 2021). Although PLS-SEM was originally designed to facilitate theory development through the exploration of data (Richter Sinkovics, et al. 2016; Ringle et al. 2020), researchers still need to bear in mind that an inductive approach in quantitative analysis requires sound conceptualization and operationalization and an adequate execution of data collection (Sinkovics 2018; Trochim 1989; Zeng et al. 2021; Wible and Sedgley, 1999). Therefore, it is important to focus more attention on methods and techniques that can be applied at the beginning of the research process to pave the way for generating sound PLS models and to enhance the benefits of theorizing with PLS (cf., Sinkovics 2016, 2018; Sinkovics et al. 2021b).
In this study, we draw on the pattern-matching framework to demonstrate how the use of qualitative techniques can strengthen the conceptualization and theorybuilding aspects of PLS. The overall pattern-matching process can be divided into three stages: partial, flexible, and full pattern matching (see Appendix Fig. 5 for an overview). By bringing together all three stages, our study demonstrates how they inform each other (see Fig. 1), specifically, how the first two stages support the development of a PLS model and the subsequent theorizing based on the empirical findings from 215 firm responses to a survey.
Multinational enterprises (MNEs) frequently interact with sociopolitical stakeholders such as civil society organizations (CSOs) across their home as well as host countries (Sun et al. 2021). These interactions are considered an aspect of MNEs' non-market strategies, and they contribute to MNEs' competitiveness by reducing challenges associated with social, political, and institutional contexts (Mellahi et al. 2016). A growing number of international business studies have highlighted non-market strategies as an integral part of MNEs' overall international business strategy (Boddewyn and Doh 2011; Cuervo-Cazurra et al. 2014; Doh et al. 2015, 2017; Kobrin 2015). Lucea and Doh (2012) propose that if MNEs are to design non-market strategies that appropriately fit their non-market context, they need to pay attention to four sociopolitical dimensions, namely, stakeholders, issues, networks, and geography. Therefore, there is a need to match what we know about these dimensions in frequently explored research settings such as the United States and Europe to knowledge generated in less frequently explored settings such as South Korea and other Asian and African geographies (cf., Doh et al. 2015). An additional factor that adds urgency to these explorations is the United Nations' (UN 2015) stance on the importance of cross-sector partnerships for the attainment of the sustainable development goals (Backstrand 2006). In this paper, we define business-CSO collaboration as "a system of formalized cooperation between several institutions [involving at least one firm and one CSO], based on a legally contracted or informal agreement, links within cooperative activities and jointly adopted plans" (Wyrwa 2018, p. 123).
We chose South Korea as our research context because of the highly influential role that CSOs play in the country's political and business environment. Understanding this context could help foreign MNEs in South Korea reduce institutional distance and design better non-market strategies. For instance, Kim et al. (2013) highlight a comment by a South Korean Corporate Social Responsibility (CSR) manager: "we get too much political influence on CSR. I think this is typical in Korea ...so businesses are not free to do what they think they should do anymore. Businesses have to pay attention to these pressures (from CSOs)" (p. 2584). As exemplified by this quotation, an important characteristic of South Korean CSOs is their active and direct participation in politics, both at an individual and group level. Many even become politicians themselves, and as a group they have been involved in the conception and running of past administrations.
Therefore, on a conceptual level, our study contributes to the international business and international management literature by furthering understanding of how the factors that drive the formation of business-CSO collaborations influence firms' collaborative behavior and ultimately the outcomes of collaboration in the South Korean context. The findings hold important implications for MNEs aiming to expand into the South Korean market given the extent to which CSOs actively shape the sociopolitical environment. Further, an understanding of how South Korean firms engage with CSOs in their home country will aid future theorizing about their collaborative behavior with CSOs in host countries.
2 Partial and Flexible Pattern Matching to Pave the Way for Structural Model Specification
Appendix Fig. 5 provides an overview of the three stages of pattern matching. Partial pattern matching is completed either in the theoretical realm, where the researcher works with the literature to identify initial theoretical patterns, or in the observational realm, where the researcher starts with empirical observations to identify theoretical patterns (Bouncken et al. 2021; Shah and Corley 2006; Sinkovics 2018). Flexible pattern matching builds on partial pattern matching (Sinkovics 2018), either within the same study or in a subsequent study. Initial theoretical patterns are deduced from the literature or a previous inductive study and matched to observed patterns in empirical data. Therefore, flexible pattern matching combines a deductive and an inductive component. In other words, it seeks to identify matches and mismatches between initial expected patterns based on the literature and observed patterns that emerge from the empirical data while simultaneously allowing new patterns to emerge from the data (Bouncken et al. 2021; Sinkovics 2018). The third stage of pattern matching is full pattern matching. This aims to determine which alternative theory best explains an empirical observation. Structural equation modelling arguably represents the highest level of full pattern matching to date because it involves pattern matches at the structural level as well as the measurement level (cf., Hair et al. 2017; Sinkovics 2018).
Figure 1 demonstrates how we used the different stages of pattern matching to construct our structural (and later, measurement) model. Step 1 involved a systematic literature review (see Appendix Table 5 for the protocol). The aim was to obtain an understanding of what is known in the literature about business-CSO collaborations and what theories are commonly used to underpin investigations on this phenomenon. We conducted this literature review with the aim to derive an initial framework - that is, a collection of theoretical dimensions as a starting point for our structural model specification. In step 2, we conducted interviews and applied a flexible pattern-matching analysis technique to check the relevance of the theoretical dimensions that we had identified from the literature to our study context. This was necessary since most studies we identified were conducted in the United States and Europe, and we needed to ascertain that those insights were relevant for our South Korean context. Flexible pattern matching further allowed us to explore whether there were any theoretical dimensions that the literature had not yet uncovered but that were important in this context. Step 3 then entailed the finalizing of our structural model and the hypotheses.
Appendix Table 7 lists the main theoretical dimensions, the corresponding operationalizations, and the expected theoretical patterns that we identified from the literature review. The three main dimensions correspond to the three phases of business-CSO collaborations: (1) formation, (2) implementation, and (3) outcomes (e.g., Selsky and Parker 2005). The formation phase refers to the factors that drive organizations to collaborate with CSOs. Prior studies suggest two main drivers (e.g., Dahan et al. 2010; Weber et al. 2017): The pressure from external stakeholders to collaborate with CSOs and the desire to gain access to the resources of CSOs. These drivers are linked to two main theories in the literature - stakeholder theory and the resource-based view.
The implementation phase of business-CSO collaborations may be linked to the collaborative behavior displayed by partners to achieve their shared objectives (Heckman and Guskey 1998). Our review of the literature uncovered three main concepts: interorganizational connectivity, shared resources, and trust in CSOs' competence and good intentions (Jiang et al. 2015; Rivera-Santos and Rufin 2010; Weber et al...
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