یادگیری سازمانی و موفقیت CRM: مدلی برای لینک کردن روشهای سازمانی،کیفیت داده مشتری و عملکرد
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : یادگیری اکتشافی، یادگیری استثمارگرانه، بخشندگی محیط زیست، روابط مدیریتی، عملکرد محصول جدید،, Volume 27, Issue 1, February 2013, Pages 1–13
A high quality customer database is a cornerstone of successful interactive marketing strategies and tactics. Based on the notion that customer data quality is not only a technical but also an organizational problem, this study develops and tests an organizational learning framework of the relationship between organizational processes, customer data quality and firm performance. The findings show that high quality customer data impact both customer and business performance and that the most important driver of customer data quality comes from the executive suite. A large portion of the impact of organizational culture on performance is mediated by customer data quality and data sharing. The results support the presence of a hierarchy of effects for enhancing data quality that runs from organizational learning (committed to a shared vision for CRM data), to cross-functional learning (marketing/IT cooperation, marketing/IT integration) to functional learning (data sharing).
There is little doubt that a high quality customer database is a fundamental requirement for developing and implementing effective interactive marketing strategies (Blattberg et al., 2009 and Malthouse and Hofacker, 2010). As a proprietary resource, customer data offer businesses the opportunity to capture competitive advantages by developing multi-channel initiatives designed to acquire and maintain close relationships with customers (Kumar et al., 2009 and Ramani and Kumar, 2008). The recent emergence of Customer Relationship Management (CRM) systems has focused even more attention on the value of customer data as a key organizational asset (Reimann, Schilke, and Thomas 2010). Through optimal resource allocation and marketing mix optimization (Kumar and George 2007), the anticipated outcomes of personalized, data-driven relationships include increased retention, share-of-wallet, customer lifetime value, and profitability (Pfeifer and Ovchinnikov 2011). Although the relationship building opportunities that data-driven initiatives offer customer-centric organizations are apparent, companies differ widely in terms of the quality of their customer data (Krasnikov, Jayachandran, and Kumar 2009). Less successful firms tend to have organization-wide data quality concerns across all avenues of their CRM systems including transactional data, customer touchpoint data, contact management data, and retention and loyalty data (Verhoef et al., 2010 and Zahay et al., 2012). There is thus a growing belief that customer data quality is not only a technical problem but also an organizational problem (Even et al., 2010 and Homburg et al., 2008). Despite this realization, relatively little research has examined how organizational policies and processes impact the quality of a firm's customer database and CRM performance (Rust, Moorman, and Bhalla, 2010). Common organizational causes of inferior customer data include horizontal communication silos, internal turf battles over ownership of customer information, a failure to share information within and across functional areas, and the lack of an organization-wide vision for maintaining, using, and enhancing customer information (Rust et al., 2010, Seddon et al., 2010 and Zahay et al., 2004). Although the marketing literature offers insights for improving the effectiveness of CRM technologies, research is missing on how an organization adapts its customer information processes once the technology is assimilated into the organization (Verhoef et al., 2010 and Zahay and Peltier, 2008) contend that there is an urgent need for data management studies that capture insights from other disciplines including organizational behavior, change management, and technology implementation. Given these concerns, our research investigates how organizations respond to customer data quality problems through functional and cross-functional learning initiatives, and how this response impacts performance. Previous CRM research has for the most part investigated data quality and performance effects through a technical rather than an organizational lens. However, a promising stream of research in the marketing (e.g., Boulding et al., 2005, Hunter and Perreault, 2007, Jayachandran et al., 2005, Payne and Frow, 2005, Verhoef et al., 2010 and Zahay and Peltier, 2008) and management information systems (e.g., Leidner and Kayworth, 2006, Seddon et al., 2010, Wade and Hulland, 2004 and Xue et al., 2008) literature is emerging that offers guidance for how organizations enhance customer data quality and CRM performance. This study utilizes organizational learning theory for deriving hypotheses concerning how intra-firm policies, practices, and relationships impact the quality of information contained in an organization's customer database (Slater and Narver, 1995 and Zahay and Griffin, 2004). We expand the literature by examining the extent to which data quality mediates the relationship between organizational processes and performance, a piece virtually missing in the CRM literature (Even et al., 2010 and Pancras and Sudhir, 2007). We thus explore the relationship between organizational factors, data quality and organizational performance in the context of the customer-centric learning organization.
نتیجه گیری انگلیسی
This study examined the relationship between organizational learning processes, data quality and CRM success in the form of customer and business performance. Special theoretical emphasis was placed on customer data quality as a consequence of organization learning processes and an antecedent to performance. We extended the theoretical domain of data quality by conceptualizing this construct beyond technical aspects (data accuracy, timeliness, completeness and consistency) to include a CRM systems perspective for planning and executing effective interactive marketing strategies and tactics. Our definition responds to Verhoef et al. (2010) notion of viewing customer data quality as both a technical and an organizational problem, one that requires both an understanding of the types of information that are needed (i.e., transactional data, customer touchpoint data, contact management data, retention/loyalty data) and how this information is used to make sound marketing decisions. Based on this definition, we developed a measure of data quality across various customer interactions and examined how the organization can best create a learning environment in which customer high data quality can be achieved. A number of key findings emerge. First, the key role of organizational culture in terms of a commitment to and a shared vision for CRM in creating high quality data through internal processes was evident in these data. A large fraction of the impact of organizational culture on performance operates through customer data quality and its antecedent data sharing. In other words, organizational culture manifests itself in improved customer data quality which in turn leads to better performance. Second, there is a “hierarchy of effect” that runs from cross-functional cooperation through marketing-IT integration and data sharing to customer data quality. Thus, if the goal is improved customer data quality, all three links are needed as the “handoffs” between functions are critical. Lastly data quality impacts both customer and business performance. The hypothesized importance of an intra-organizational learning orientation for creating high quality customer data and improving firm performance was supported by our data. The results show that high quality customer data impact customer and business performance directly (and also business performance in a mediated fashion through customer performance), and that the most important driver of data quality comes from the executive suite. The pervasive effect of organizational culture on customer data quality is somewhat surprising because executives usually do not involve themselves in the time-consuming and detail-oriented business of data cleansing and maintenance. Rather, these functions are routinely delegated to relatively low-placed organizational minions that perform these unglamorous and often unappreciated functions far out of the eye of the executive suite. However, a culture focused on customer information improves information systems and organizational processes. Our research provides empirical support for the qualitative findings of Zahay and Peltier (2008) and highlights the need to view data quality as a collaborative process stemming from a top-down commitment to customer-centricity. In addition to direct effects, we found support for a hierarchy of effects leading from organizational learning processes to cross-functional learning processes to functional learning processes. Specifically, a shared organizational culture and vision for supporting data quality initiatives creates superior cross-functional and functional learning orientations. This aligns with Homburg, Grozdanovic, and Klarmann (2007) and Jayachandran et al. (2005) views that changing an organization's affective system (information culture) is a precursor to CRM success. Linking cross-functional to functional learning processes, our findings show cooperation between the IT and marketing departments in particular increases the likelihood that project priorities are integrated, data are shared and customer data quality is increased. Everyone benefits when marketing and IT not only cooperate and set mutual priorities and goals but also agree on detailed project priorities. We thus extend Homburg et al. (2007) and Sykes, Venkatesh, and Gosain (2009) by showing that effective knowledge management transfer is accomplished through positive learning interactions between functional units. Building upon recent conceptual work by Even, Shankaranarayanan, and Berger (2010), our findings underscore the critical role that the marketing department plays in improving the quality of data repositories. Consistent with Slater and Narver (1995) and Peltier et al. (2006), we argued that data sharing is an organization learning mechanism that is contingent on how well information is disseminated, interpreted, and altered over time. Our findings show that data sharing has a prominent position in organizational learning and data quality, and in fact is the only factor other than a corporate vision for customer information that relates positively and directly to customer data quality. Data sharing is not only impacted by organizational culture; it lies on a key path between marketing-IT cooperation and marketing-IT integration and thus serves as the final link between the other learning antecedents and data quality. Moreover, our results suggest that customer data quality and CRM system success are contingent on the ability of data users and decision makers to access databases when and where they need them. This is consistent with the Resource-Based View of the firm, which states that it is not a firm's resources that are a source of competitive advantage but rather how these resources are managed and shared (Day 1993). In spite of decades of database development, the legacy systems and the associated data ‘silos’ that abound in organizations will disappear, if and only if, the development of shared systems becomes an organizational priority at the highest level. This study expands the focus of CRM success by investigating data quality and performance effects through an organizational lens. Building upon marketing (e.g., Zahay and Peltier, 2008 and Verhoef et al., 2010) and management information systems (Seddon, Calvert and Yang 2010) literature, our research found evidence that data quality mediates the impact of data sharing and organizational culture on customer and business performance. We provide evidence that data quality is a critical ingredient driving superior customer relationship efforts leading to financial success, in line with Even, Shankaranarayanan, and Berger (2010) who suggest that data quality and cost/benefit analyzes are part and parcel of an organization's information optimization processes.