With the development of internet and communication technologies, diversification of consumer needs, and intensifying competition, omnichannel is spotlighted among retailers as one of the breakthroughs to survive. Particularly, among scholars and practitioners, cross channel integration (CCI) has been pointed out as a key concept to achieve success in omnichannel, and several successful cases of retailers and strategies implemented under CCI are reported. Nevertheless, due to the wide scope and complexity of CCI, as well as the substantial cost and long-term perspective required for its investment, CCI involves a high level of uncertainty and complicated decision-making. Thus, most retailers are still struggling in achieving the level of CCI that their consumers desire.

Introduction An important decision that a manufacturer has to make in distributing a product to customers is the degree of forward channel integration (Aulakh & Kotabe, 1997; Coughlan et al., 2001; John & Weitz, 1988). Transaction cost economics (TCE) developed by Williamson (1975, 1985, 1986, 1999) has been one of the leading theoretical frameworks used to explain the channel integration decision (Frazier, 1999; Watson et al., 2015). TCE is generally a theory for explaining the choice of an efficient governance structure in transactions and includes asset specificity, uncertainty, and frequency as its explanatory variables. According to Williamson (1985, 1986, 1999), much of the explanatory power of TCE is driven by asset specificity. TCE-based channel integration studies argue that as asset specificity increases, firms are expected to increase the degree of channel integration. This study proposes to extend existing research in four important ways. First, existing studies have not examined individual dimensions of asset specificity. This study examines two important dimensions discussed by TCE: human asset specificity and physical asset specificity. Second, existing studies have tended to measure asset specificity in a particular way (i.e., with a particular set of questionnaire items). This study examines the robustness of the estimated asset specificity-integration relationship to alternative measures of asset specificity. Third, existing studies have focused on firms in one country such as the United States, Canada, or Germany. This study empirically examines the roles and relative importance of human and physical asset specificity in channel integration in two countries with different cultures, the United States and Japan. Fourth, existing studies have not investigated the possibility of endogeneity between asset specificity and channel integration. This study tests whether asset specificity is endogenous in explaining channel integration through an instrumental variables and two-stage least squares (IV-2SLS) approach. Literature Review In the context of distribution channels, asset specificity refers to the extent to which durable, transaction-specific investments in human and/or physical assets are needed to distribute the product in question (John & Weitz, 1988; Klein et al., 1990; Shervani et al., 2007). Examples of such investments include (1) the time and effort employed to acquire the firm-specific, product-specific, and customer-specific knowledge needed for distribution activities, and (2) specialized physical equipment and facilities (e.g., warehouses, deliver vehicles, refrigeration equipment, demonstration facilities, and repair and service centers) (Anderson, 1985; Bello & Lohtia, 1995; Brettel et al., 2011a, 2011b; John & Weitz, 1988; Shervani et al., 2007; Williamson, 1985, 1986). According to TCE, when the assets needed to distribute a product are non-specific, the use of independent channels is a priori more efficient than the use of integrated channels based on the benefits of distribution specialists and competition in the market place (Anderson, 1985). Conversely, a high level of specific assets, whether human or physical, has important implications for the degree of channel integration. The primary consequence is to reduce a large number of relationships between a manufacturer and independent channel members to a small number of relationships, which may expose the transaction in question to opportunistic behavior. Because the unique productive value created by a high level of specific assets makes it costly to switch to a new relationship, the use of independent channels will not be effective as a safeguard against opportunism (John & Weitz, 1988; Shervani et al., 2007). Channel integration provides a safeguard against opportunism by permitting (1) the better monitoring and surveillance of integrated channels relative to independent channels, and (2) the reduction of profits from opportunistic behavior since employees in integrated channels do not ordinarily have claims to profit streams (John & Weitz, 1988). As a result, as asset specificity increases, manufacturers are expected to increase the degree of channel integration to exercise greater control over the channels (John & Weitz, 1988; Shervani et al., 2007). This leads to the following basic TCE hypothesis concerning asset specificity and channel integration: TCE hypothesis. Asset specificity will be positively related to the degree of channel integration. Existing studies of channel integration tend to provide support or partial support for the hypothesized positive relationship between asset specificity and channel integration. One limitation of key studies is that they have not fully explored the dimensions of asset specificity because they treat asset specificity as unidimensional or examine only one dimension of asset specificity. Specifically, Anderson and Schmittlein (1984), Anderson (1985), Anderson and Coughlan (1987), and Krafft et al. (2004) focus on human asset specificity. While John and Weitz (1988), Shervani et al. (2007), and Brettel et al. (2011a) consider both human and physical asset specificity in their theoretical discussions, their empirical analyses focus only on human asset specificity. Klein et al. (1990), Aulakh and Kotabe (1997), and Brettel et al. (2011a) use a single measure of asset specificity that contains distinct items measuring human and physical asset specificity. Importantly, none of these studies has examined the dimension of physical asset specificity while controlling for the impact of human asset specificity. These observations suggest that further research is needed that explicitly measures and evaluates the relative importance of human and physical asset specificity in the channel integration decision. Research Hypotheses Based on the above literature review, we seek to extend existing research by distinguishing between two types of asset specificity, human and physical asset specificity. As already explained, TCE and TCE-based channel integration studies argue that both human and physical asset specificity are positive drivers of the degree of channel integration. Thus, our research hypotheses are the following: Hypothesis 1. Human asset specificity will be positively related to the degree of channel integration. Hypothesis 2. Physical asset specificity will be positively related to the degree of channel integration. Research Methodology As shown in Table 1, previous empirical studies attempt to test the basic TCE hypothesis concerning asset specificity and channel integration using (1) a particular measure of asset specificity, (2) data from a single national survey of firms in the United States, Canada, or Germany, and (3) methods such as an ordinary least squares (OLS) regression analysis and a partial least squares structural equation modelling (PLS-SEM) approach. In contrast with these studies, we seek to test the above two hypotheses concerning two types of asset specificity and channel integration using (1) different measures of asset specificity, (2) data from parallel national surveys of firms in two countries with different cultures, the United States and Japan, and (3) the methods used in prior empirical analyses and an IV-2SLS approach, which is a widely accepted method for investigating the potential endogeneity problem of focal explanatory variables (Antonakis et al., 2010, 2014; Zaefarian et al., 2017). This research strategy is partly based on the guidelines for high-quality replication studies articulated by Bettis et al. (2016b). The aims are to assess the generalizability of important prior results using different survey data drawn from different research contexts and to assess the robustness of these results using different measures and methods, thereby providing important additional evidence that contributes to the establishment of repeatable cumulative knowledge (Bettis et al., 2016a, 2016b). We developed the survey questionnaire in several steps. Following John and Weitz (1988), Shervani et al. (2007), and Brettel et al. (2011b), the dependent variable, channel integration, was operationalized by the percentage of sales through direct channels. We measured the focal explanatory variable, asset specificity, in four ways: (1) a four-item scale of human asset specificity used by Shervani et al. (2007), (2) a four-item scale of physical asset specificity based on Bello and Lohtia (1995) and Klein et al. (1990), (3) a six-item scale of human and physical asset specificity used by Klein et al. (1990), and (4) a four-item scale of human and physical asset specificity used by Brettel et al. (2011a). We also included four control variables: environmental uncertainty, behavioral uncertainty, financial performance, and channel members’ capabilities. Based on existing studies, manufacturers of electronic and telecommunication, metal, and chemical products in industrial (business-to-business) markets were selected as the setting for the empirical test. The unit of analysis was the domestic channel integration decision made at a product-market level. Respondents were sales/marketing managers (or executives) knowledgeable about channel design and strategies. In the United States, a professional marketing research company administered the data collection. In Japan, respondents were surveyed by mail. In total, we obtained 235 usable responses from US managers and 279 responses from Japanese managers. Results and Conclusions Following similar studies (John & Weitz, 1988; Shervani et al., 2007), an OLS regression analysis was used to test the hypotheses. The results, shown in Table 1, exhibit significant explanatory power for each model. As expected, (1) human asset specificity exhibits significant positive relationships with the degree of channel integration in both the United States and Japan (Models 1 & 2). These findings support Hypothesis 1. Conversely, (2) physical asset specificity does not have the expected significant positive relationships with the degree of channel integration in both the United States and Japan (Models 1 & 3). These findings do not support Hypothesis 2. Also, (3) asset specificity (Klein et al., 1990) and (4) asset specificity (Brettel et al., 2011a), two composite measures of human and physical asset specificity, exhibit the expected significant coefficients (Models 4 & 5). Additionally, we conducted a similar analysis using a structural equation modelling approach. The results mirrored those of OLS regression, thus providing further support for it. To assess the problem of potential endogeneity between asset specificity and channel integration, we employed IV-2SLS. We used (1) the level of the product’s technical content and (2) the need for coordination between production and distribution activities as instruments for human/physical asset specificity. Our instruments were individually significant predictors of asset specificity and met the exclusion restriction. However, the endogeneity test revealed no evidence of endogeneity. Thus, asset specificity was treated as exogenous in the model. In summary, our preliminary results suggest that human asset specificity, not physical asset specificity, is relevant to the channel integration decision. This finding is significant in that TCE-based channel integration studies tend to measure only one type of asset specificity. We are currently conducting additional analyses to better understand the relationship between human and physical asset specificity, for example, (1) the effects of human and physical asset specificity on different kinds of direct distribution, and (2) a multiple equation model in which human asset specificity is a function of physical asset specificity and direct distribution is a function of both human and physical asset specificity. We believe that our results will have important implications for the ways in which managers approach the channel integration decision.

Since the arrival of omni-channel retailing, which promotes seamless experience for consumers and zero effort commerce, channel integration has been a big issue in both the domestic and the international retail industry. Some researchers have identified problems that can occur in the process of channel integration, such as cannibalization and channel conflict (Coelho & Easingwood, 2003). However, many studies on channel integration report its positive impact on a firm’s revenue growth through improved trust (Schramm-Klein & Morschett, 2006), higher consumer conversion rates (Neslin et al., 2006), and greater cross-selling opportunities (Berry et al., 2010). Regarding the issue of effectively establishing channel integration in order to bring positive synergy to a company, the present study intends to identify a solution within a company’s internal factors. This study aims to provide a strategic perspective on channel integration formation of domestic fashion retailers by identifying some of the key organizational components that drive a firm’s channel integration in this omni-channel era, when the boundaries between online and offline markets are disappearing. This study predicts that organizational structure and strategic orientation are the key components of a fashion retailer’s channel integration implementation in an omni-channel environment. As shown in previous studies, channel integration has a positive impact on a firm’s performance through active and innovative transformation of the organization’s hardware and software (Cao & Li, 2015; Yan, Wang, & Zhou, 2010). In particular, this study introduces channel (extension) strategies (number of different types of channels in both online and offline markets) into channel integration as one of the crucial variables, in addition to the two existing variables. The data were collected through a survey targeting mid-level executives or above, within a business unit of Korea’s fashion companies with over $10 million revenue. Through this selection, a total of 120 samples were used in the final analysis. Hierarchical regression modeling was used to prove the study’s hypothesis. The revenue size of a parent company and SBU was used as a control variable in the level 1 model; channel strategies in the level 2 model; organizational structure in the level 3 model, and organization strategic orientation in the level 4 model, which was used as an independent variable. Integrated back-end system and integrated human resource management, which are the highest levels of channel integration (Cao & Li, 2015; Oh, Teo, & Sambamurthy, 2012), have been used as dependent variables. The main findings of this study are as follows: In a back-end system integration model, organization strategic orientation was identified as the highest level when the organizational structure becomes more centralized, whereas the system integration level is the highest when the model is competitor-oriented and innovation-oriented. In the human resource management integration model, the human resource management integration level is at its highest when the organizational structure becomes formalized and specialized, and organization strategic behavior becomes more competitor-oriented and innovation-oriented.

본 연구는 한국 시장에 진출해 있는 외국계 제조업체들의 한국 시장내 유통경로 전략의 실체를 파악해 보고, 기존의 이론들이 한국내 외국계 기업들에도 적용될 수 있는지 알아보기 위한 시도로 이루어졌다. 이를 위해 현상 기술적인 입장에서 그들의 유통정책과 관련된 몇가지 변수들을 조사분석해 보았으며, 유통경로의 통합에 영향을 미치는 변수들을 중심으로 가설을 설정한 후 이를 검정하였다. 분석 결과, 외국계 제조업체들의 유통경로 정책에는 본국요인보다는 현지국 요인이 더 크게 작용하는 것으로 보이는 측면들이 많이 발견되었다. 한편 통합경로 선정에 영향을 미치는 변수들을 알아보기 위한 로짓분석 결과, 거래비용 접근법에 의해 설정된 거래특유 자산 (transaction-specific assets) 변수가 통합경로의 선택에 正의 영향을 미친다는 것이 다시 한번 발견되었다. 또한, 본 연구에서 탐험적으로 시도해 보았던 기능적 접근법의 적용 결과, 외부 유통업자들이 유통기능을 수행하는 능력이 높다고 인식될수록 통합경로보다는 독립적 유통업자를 선택할 가능성이 높아진다는 가설이 부분적으로 지지되었다.