اهرم قدرت یادگیری بر قابلیت عملیات تولید
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|21326||2013||20 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, Volume 145, Issue 1, September 2013, Pages 233–252
Learning capability (LC) is a special dynamic capability that a firm purposefully builds to develop a cognitive focus, so as to enable the configuration and improvement of other capabilities (both dynamic and operational) to create and respond to market changes. Empirical evidence regarding the essential role of LC in leveraging operational manufacturing capabilities is, however, limited in the literature. This study takes a routine-based approach to understand capability, and focuses on demonstrating leveraging power of LC upon two essential operational capabilities within the manufacturing context, i.e., operational new product development capability (ONPDC), and operational supplier integration capability (OSIC). A mixed-methods research framework was used, which combines sources of evidence derived from a survey study and a multiple case study. This study identified high-level routines of LC that can be designed and controlled by managers and practitioners, to reconfigure underlying routines of ONPDC and OSIC to achieve superior performance in a turbulent environment. Hence, the study advances the notion of knowledge-based dynamic capabilities, such as LC, as routine bundles. It also provides an impetus for managing manufacturing operations from a capability-based perspective in the fast changing knowledge era.
In strategic management literature, organizational routines have been perceived as the foundation of capabilities (Eisenhart and Martin, 2000, Nelson and Winter, 1982 and Teece, 2007). These routines are broadly defined as regular and predictable patterns of behaviors, or the way work is done (Teece et al., 1997), and have a wide range of variations. Some are constantly changing, while others are relatively static, which indicates the underlying phenomena and dynamics (Pentland and Feldman, 2005). Static operational capabilities are created by a collection of operating routines that execute procedures for the purpose of generating current revenue and profit (Nelson and Winter, 1982 and Zollo and Winter, 2002). Dynamic capabilities are created by a collection of search routines that bring about desirable changes in the existing set of operating routines or the development of new ones, in order to sustain competitive advantage in a rapidly changing environment (Helfat et al., 2007 and Kyläheiko et al., 2002). In other words, operational or ‘zero-level’ capabilities are those that permit a firm to generate revenue and profit, in the short term, while dynamic capabilities are ‘higher-level’ capabilities that operate to extend, modify or create operational capabilities for the purpose of enhancing profit in the future (Winter, 2003 and Zollo and Winter, 2002). It has been asserted that deliberate organizational learning is responsible for modifying and renewing both dynamic and operational capabilities, over time (Kyläheiko et al., 2002 and Zollo and Winter, 2002). Accordingly, knowledge-based learning capability (LC) is perceived as a highly intelligent dynamic capability that enables both knowledge exploration and exploitation (Azadegan and Wagner, 2011 and March, 1991). The process facilitates the modification and configuration of capabilities, in particular, the operational capabilities (Nooteboom, 2009). The strategic importance of LC hence lies in its ability to create cognitive mechanisms that can innovatively respond to market changes. The advent of rapidly advancing information technologies and fierce global competition has changed the traditional business models of manufacturing firms. Innovative new product development (NPD) and supplier integration have become underlying routines of essential operational manufacturing capabilities to effect performance outcomes (Marsh and Stock, 2006 and Terpend et al., 2008). The degree to which operational capabilities produce superior performance appears to be affected by a certain collection of underlying routines of LC (e.g., Allred et al., 2011, da Silva Gonçalves Zangiski et al.,, Hull and Covin, 2010, Li et al.,, Pavlou and El Sawy, 2011 and Peng et al., 2008). The leveraging power of learning contingencies upon the core manufacturing operational routines has been proposed (Azadegan et al., 2008). However, little research has been undertaken into how organizational learning engenders and modifies operational capabilities as bundles of interrelated yet distinct routines. In view of this research need, the current study aims to investigate the leveraging power of LC in enabling operational NPD capability (ONPDC), as well as operational supplier integration capability (OSIC) to effect performance outcomes within a turbulent manufacturing industry. The study sought to answer two research questions: (1) does LC moderate the relationships between operational manufacturing capabilities (i.e., ONPDC and OSIC) and performance outcomes? (2) How do certain underlying routines of LC reconfigure and modify specific underlying routines of ONPDC and OSIC within various manufacturing contexts? Rather than focusing on producing an exhaustive set of measures for the capabilities under investigation, the primary objective of the study was to demonstrate how certain underlying routines of LC could be manipulated by managers and practitioners to redesign and enable specific operational routines of NPD and supplier integration, and so better match the market environment. To fulfill the research objective, a mixed methods research framework (Morse, 2003 and Yin, 2009) was adopted. It combined the evidence derived from multiple sources, using quantitative and qualitative data collection and analytical techniques, in sequential phases. Building upon the capability assertions as well as empirical evidence, established within the manufacturing context, the survey study was undertaken to empirically identify significant moderating effects of particular underlying routines of LC on those of ONPDC and OSIC, thereby providing answers for the first research question. An explanatory multiple-case study was subsequently undertaken to provide answers for the second research question. The impetus for adopting the case study approach stemmed from the need to reveal the underlying insights of the relationships identified within real-life manufacturing contexts, as well as to uncover contextual conditions, which potentially influence the strength of modifying effects of LC. From a theoretical perspective, the study advanced the notion of knowledge-based dynamic capabilities, for example LC, as routine bundles, which enable manufacturing routines to robustly handle a turbulent business environment. The study not only identified specific high-level learning routines that could be manipulated by managers and practitioners to leverage their core operational manufacturing routines, but also highlighted the contextual conditions that potentially influence the degree of the leveraging effect. The findings have significant implications for manufacturing operations. The remainder of the paper is structured as follows. Based on the literature review, the next section addresses the strategic importance of LC and posits its leveraging power, which matches ONPDC and OSIC with the market needs in a constantly changing environment. The mixed methods research framework is then presented, followed by the data analysis of both the survey study and the multiple-case study. The paper concludes with a discussion on the theoretical contributions, managerial implications and future research directions of LC in manufacturing operations.
نتیجه گیری انگلیسی
This study was motivated to demonstrate the leveraging power of LC upon essential operational manufacturing capabilities (i.e., ONPDC and OSIC). The study also sought to identify high-level learning routines that can be manipulated by managers and practitioners, so as to reconfigure underlying routines of ONPDC and OSIC for the purpose of effecting superior performance within a turbulent environment, over time. A mixed methods research framework (that combined evidence derived from a survey study and a multiple case study) was used to fulfill the research objective. Recent assertions about the knowledge-based dynamic capability (Lewin et al., 2011, Lichtenthaler and Lichtenthaler, 2009 and Nooteboom, 2009) are congruent with the survey study in its support of LC. This was particularly so with this dynamic capability being able to leverage the two operational capabilities to effect better business performance. Indeed, the regression analyses at both the construct and factor levels demonstrated the positive moderating effects of LC on the contribution of both operational capabilities (i.e., ONPDC and OSIC) to performance outcomes. Furthermore, the analysis at the factor level highlighted that IT applications significantly leverage design simplification and modular design, as well as supplier evaluation and selection, to improve business competitiveness. This outcome is indicated by a number of bottom line indicators, such as profitability increase, total quality cost reduction, and sales growth. In addition, the comparison of the leveraging power of LC within the context of NPD, and that of supplier integration, reveals a stronger moderating effect of the learning routines upon ONPDC. The case study provided important insights that explain how LC modifies and reconfigures underlying routines of ONPDC and OSIC, so that firms can respond to the dynamic changes in the market. The interviews revealed that the manufacturing firms developed complex complementarities between the internal and external learning routines to form an appropriate cognitive distance, which enabled exploratory and exploitative learning. Additionally, the case study found that the history of the case firms decided, to a large degree, the underlying learning routines of their LC. The contextual factors also influenced the complex social mechanisms that enabled the deliberate learning of the firms. Causal ambiguity of LC was also evident during the interviews, where some experienced managers had difficulty clearly articulating the performance implications of certain learning routines that deal intensively with tacit knowledge. The findings imply that LC in the case firms was idiosyncratic; it was also hard to imitate due to the path dependence, social complexity and causal ambiguity. The interviews revealed that the underlying routines of ONPDC and OSIC of the case firms were constantly reconfigured and modified by higher-level learning routines. While some firms depended primarily on internal innovation to generate new designs that differentiated their products and adopted exploratory learning strategies; others tended to leverage customer and supplier design advantage in NPD and used exploitative learning strategies. The findings suggest that the leveraging power of LC created firm-specific ONPNC and OSIC, which became valuable resources that effected superior performance outcomes. Furthermore, NPD appeared to involve higher levels of knowledge exploration that relied much more on the cognitive mechanisms provided by LC to generate innovation; in contrast supplier integration routines depended on LC primarily for the exploitation of knowledge in the supply chain. These findings explain the different moderating effects of LC upon ONPDC and OSIC, as identified by the regression analyses. Additionally, the case study also uncovered the contextual factors that affected how a particular case firm developed its LC through various combinations of knowledge exploration and exploitation strategies. These factors included the supply chain structure, the organizational culture of the firm and its partners, the project's nature, and the product and service types, especially the tacitness of production and services knowledge. In the real world industrial situation, these factor also affected the degree to which the case firms’ LC modified ONPDC and OSIC. The results, derived from the mixed methods study, suggest that certain high-level learning routines of LC can be used to modify and configure the underlying routines of ONPDC and OSIC. Further, it is apparent that the approaches are context-specific. The findings imply that manufacturing firms need to place an emphasis on encouraging employees to engage in learning. A wide range of arrangements were adopted by the case firms to provide good examples of effective learning mechanisms, for example: assigning a chief knowledge officer and a dedicated unit to support learning across the entire firm; making arrangement with major suppliers, customers, and research institutes to share technical and managerial know-how; using performance indicators to assess the learning performance of each employee and provide incentives; offering strong support to the IT system; and updating frequently knowledge repository. Furthermore, since LC is an intelligent high-level capability, its underlying routines should be embedded into NPD and supplier integration routines to effect reconfiguration and evolution. In other words, learning programs should be promoted and designed as an integral part of NPD and supplier integration plans within a firm's overall strategic management consideration.