کاوش در الگوهای اساسی سرمایه گذاری های مشترک مرتبط و غیر مرتبط با استفاده از شبکه های عصبی: تحقیقات تجربی از داده های شکل گیری سرمایه گذاری مشترک پوشا 1985-2001
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|20082||2007||28 صفحه PDF||سفارش دهید||12333 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Business Review, Volume 16, Issue 6, December 2007, Pages 659–686
Collaborative ventures—both equity-based partnerships as well as project-based alliances—have dominated the international business scene over the past two decades. By means of this study we investigate the patterns of related and unrelated collaborative venture formation. Using a large database of over 90,000 collaborative ventures formed during the 1985–2001 period, this study clusters collaborative ventures on the basis of the industry group and home country relatedness of the collaborating partners. Self-organizing map technique within neural network methodology is used to accomplish this objective. The clusters obtained from the self-organizing map form the basis for developing taxonomy of collaborative ventures in which the neurons underlying clusters are classified based on the country of origin and industry affiliations of the collaborating partners and the collaborative venture. The distinguishing characteristics of the clusters and the taxonomy help augment our current understanding of the formation of collaborative ventures.
Alliance partnerships have been an area of intense scholarly investigations. Researchers have suggested varying rationale for determining the choice of alliance partners. One group of scholars contends that complementary core competencies are central to alliance formation (e.g. Collis & Montgomery, 1995; Hennart & Reddy, 1997; Peteraf, 1993; Silverman, 1999). The underlying rationale here is the synergy that results from resource and skill complementarities between the partners (Das & Teng, 2000). Others argue that the degree of similarity between the partnering firms is positively related to alliance formation and performance (e.g. Akhavein, Berger, & Humphrey, 1997). The similarity argument is rooted in the notion of “relatedness,” both in terms of industry affiliations and home country relatedness. Industry and home country frequently are cited as factors that are indicative of relatedness among firms engaged in collaborative ventures (e.g., Palich & Gomez-Mejia, 1999; Silverman, 1999). These variables are commonly examined in terms of the closeness between partners’ industries or home country (e.g., Kogut & Singh, 1988) as well as the closeness between parent firms and their collaborative ventures (Merchant & Schendel, 2000). Most researchers argue that relatedness can be regarded as a distance. For example, cultural distance (Kogut & Singh, 1988) captures the degree to which various countries are related. While the relatedness concept has contributed to our knowledge of alliance formation, it also raises a key question: if a joint venture (JV) is in an industry related to one parent but not the other, should this partnership be classified as a related or an unrelated collaborative venture? Some attempt has been made to address this question. Reuer and Koza (2000), for instance, categorize JVs into four groups: parents and JV in the same industry, parents in the same industry but JV in a different industry, JV in the same industry with one parent, and parents and JV in different industries. It was found that only the last two groups are associated with positive shareholder values. This research points to the importance of approaching the relatedness factor from a multidimensional perspective. Furthermore, this issue is compounded by an increasing evidence of alliance formation between partners from somewhat unrelated industries and different countries (Reuer & Koza, 2000; Steensma & Lyles, 2000). Consensus has yet to be reached as to which of the competing arguments is a valid explanation for alliance formation. Hence, it is important to explore this issue in a generalized setting. The present research represents an attempt towards such an exploratory examination. Specifically, we explore the underlying clusters in collaborative venture formation and their associated characteristics using JV data from 1985–2001. There are three research objectives for this study: • Explore the underlying patterns in collaborative venture formation during 1985–2001 by allowing the data to self-organize into distinct clusters. • Develop taxonomy of collaborative ventures formed during the 1985–2001 period based on industry and country of origin of collaborating partners and the collaborative venture. • Delineate the distinguishing characteristics of the various classes underlying the taxonomy. A preliminary examination of the dataset indicated that the required assumptions for applying statistical techniques were not met. Hence, the use of econometric and psychometric approaches was considered inappropriate for clustering the data and to develop the taxonomy. We employ neural networks to detect patterns in collaborative venture formation and to examine the relationship between collaborating partners’ and the collaborative JV's industry and national affiliation. Neural networks have recently gained increasing visibility in business research (e.g. Hu, Zhang, & Chen, 2004; Lin, Chen, & Nunamaker, 1999/2000; Montagno, Sexton, & Smith 2002; Smith & Aggoune 2003; Veiga, Lubatkin, Calori, Very, & Tung, 2000; Wray, Palmer, & Bejou, 1994). Among the various neural network techniques, we use the unsupervised learning technique called self-organizing maps (SOM). This technique does not require any prior model specifications and does not have to satisfy the statistical assumptions, thus making it appropriate for our exploratory study. The remainder of this article is organized as follows. We begin with a brief review of the literature and then present our conceptual framework in Section 2. In Section 3, we discuss the research design, methods and the data. 4 and 5 presents the results of our analysis, which is followed by a discussion of key implications of these results. Finally, in Section 6, we offer conclusions and suggest directions for future research.
نتیجه گیری انگلیسی
The present study advances our knowledge of collaborative venture formation in two important ways. First, we provide an empirical investigation of a large dataset of collaborative venture formation for an extended period of time to unravel the underlying patterns. We present self-organizing maps as an approach to examine these patterns and delineate the clusters comprising of code vectors that are strongly related. We explain the underlying characteristics of these clusters and present some insights regarding collaborative venture formation during the 1985–2001 time period. Second, we present taxonomy, which explicitly considers the continuum of relatedness when viewed from the perspectives of industry groups and home countries. The taxonomy provides an understanding of the overall pattern in which collaborative ventures were formed by grouping the code vectors into appropriate categories. There are a few limitations of this study. First, although we examined data from more than 65 industries and 200 countries, we had to keep the input data at a manageable level by grouping industries and countries into categories. This introduced some degree of subjectivity into the analysis. Second, the research offers a novel approach for recognizing industry and home country patterns, but it lacks predictive ability. Third, the data consists mostly of publicly traded companies hence the results cannot be generalized for privately held corporations. Nevertheless, the extensive dataset and the exploratory nature of the study fulfills its task by pointing to the patterns that underlie collaborative ventures for a large number of organizations. In the future, neural network methods can be expanded to cover pattern recognition for collaborations involving more than two partners. Moreover, several other possible antecedents for collaboration should be explored, such as specific management styles, prior alliance experience, and political and macroeconomic conditions in partners’ respective countries (Nath, 1988). In addition, researchers can incorporate predictive features by using such neural network techniques as feed-forward networks to predict the success of alliances under various contingencies. The results reported in this paper present an initial step towards a conceptual framework to understand collaborative ventures based on the relatedness dimension. The framework can be refined in future studies to incorporate richer measures of relatedness that extend beyond industry and home country relatedness.