|عنوان فارسی مقاله:||هموار کردن مسیر برای بازاریابی متمایز|
|عنوان انگلیسی مقاله:||Paving the way for “distinguished marketing”|
|رشته های مرتبط:||مدیریت، بازاریابی، مدیریت بازرگانی و مدیریت کسب و کار|
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|نشریه||الزویر – Elsevier|
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بخشی از ترجمه فارسی مقاله:
توسعه و یا کاربرد مدلها و ابزارهای آماری نیز در پیشرفت دانش بازاریابی سهم دارند. برای مثال، یک مطالعه اخیر، یک توالی تست آماری را توسعه داده است که اجازه تعیین اندوژنوس تغییرات بالقوه بازار که ناشی از ورودیهای رقابتی در بازارهای موجود میباشد را میدهد Kornelis, Dekimpe, &) Leeflang, 2008). سایر مثالها شامل معرفی و استفاده از مدلهای خطی دینامیک در بازاریابی Ataman,Mela, & Van Heerde, 2007, 2008; Ataman, Van Heerde, & Mela,) 2010; Van Heerde,Mela, & Manchanda, 2004)، مدلهای فضایی Bronnenberg &) (Mahajan, 2001; Van Dijk, Van Heerde, Leeflang, & Wittink, 2004، تخمین نیمه پارامتریک (Rust, 1988; Van Heerde, Leeflang, & Wittink, 2001) ، و احیای فیلترینگ کالمن میباشد ((Osinga, Leeflang, Srinivasan, & Wieringa, 2011; Osinga, Leeflang, & Wieringa, 2010).
بخشی از مقاله انگلیسی:
The development and/or application of statistical methods and tools also contribute to advance marketing knowledge. For example, a recent study developed a statistical testing sequence that allows for the endogenous determination of potential market changes from competitive entries in existing markets (Kornelis, Dekimpe, & Leeflang, 2008). Other examples include the introduction and use of dynamic linear models in marketing (Ataman, Mela, & Van Heerde, 2007, 2008; Ataman, Van Heerde, & Mela, 2010; Van Heerde, Mela, & Manchanda, 2004), spatial models (Bronnenberg & Mahajan, 2001; Van Dijk, Van Heerde, Leeflang, & Wittink, 2004), semi-parametric estimation (Rust, 1988; Van Heerde, Leeflang, & Wittink, 2001), and the “revival” of Kalman filtering (Osinga, Leeflang, Srinivasan, & Wieringa, 2011; Osinga, Leeflang, & Wieringa, 2010). Among the many promising research avenues, the modeling of the choice behavior of multiple agents and the use of agent-based modeling and social simulation are of particular interest. Examples of models that consider multiple agents are the studies of intrahousehold behavioral interactions (Aribarg, Arara, & Kang, 2010; Yang, Zhao, Erdem, & Zhao, 2010), interactions between physicians and patients in the choice of new drugs (Ding & Eliashberg, 2008), and extended interactions between manufacturers and retailers (Ailawadi et al., 2005; Villas-Boas & Zhao, 2005). Goldenberg, Libai, Moldovan, and Muller (2007) use an agentbased approach to simulate the effects of negative news about the firm and/or its products on the net present value of a firm. Combinations of empirical data and simulated data also offer key opportunities to study (individual) customer behavior in the future (Van Eck, Jager, & Leeflang, 2011a). The development of models and methods to support decision making is not without problems, however, and several issues demand more adequate answers. First, vast numbers of firms do not make data-driven marketing decisions, often because of their limited capacities (e.g., time, money, capabilities) to collect data about relevant metrics. Nor do most firms estimate relationships between the metrics they have. Subjective estimation methods would be useful tools in these cases. The development of relatively simple methods to establish connections between marketing efforts and marketing performance measures for these firms would be widely welcomed. Furthermore, even firms that can collect appropriate data face problems. Well-known modeling issues include error-in-variables, (unobserved) heterogeneity, and endogeneity (Shugan, 2006). Despite commendable progress in challenging endogeneity problems (Gupta & Park, 2009; Kuskov & Villas-Boas, 2008; Petrin & Train, 2010), many solutions remain complicated and model specific. In addition, marketing model building usually centers more on the specification and calibration of the demand side rather than the supply side. More recently, the simultaneity of demand and supply relations has received greater attention in so-called structural models (Dubé et al., 2002; Chintagunta, Erdem, Rossi, & Wedel, 2006; see also commentaries in Marketing Science, vol. 25, no. 6), which “rely on economic and/or marketing theories of consumer or firm behavior to derive the econometric specification that can be taken to data” (Chintagunta et al., 2006, p. 604). For example, Draganska and Jain (2004) estimate market equilibrium models. Kim et al. (2010) assess user demand for competing products. Liu (2010) investigates alternative pricing strategies, whereas Musalem, Olivares, Bradlow, Terwiesch, and Corsten (2010) seek to measure the effects of out-of-stock situations. These models attempt to optimize the behavior of agents, manufacturers, wholesalers, retailers, and customers. Structural models therefore offer excellent opportunities, at least in principle, (1) to test behavioral assumptions, (2) to investigate alternative strategies through policy simulations, and (3) to eliminate or reduce endogeneity problems. As outlined previously, this approach is not really new. Moreover, Chintagunta et al. (2006) demand that we recognize the drawbacks of structural models, such as their strong identification of mostly parametric assumptions, because otherwise no optimal behavior can be determined. Furthermore, builders of structural marketing models must rely on insufficiently developed theories. The structural demand model developed by Villas-Boas and Zhao (2005) illustrates one of the drawbacks. They investigate the degree of manufacturer competition, retailer–manufacturer interactions, and retailer product category pricing in the U.S. ketchup market. Their model includes multiple manufacturers and individual customers, but only one multiproduct retailer. The model also relies on several other restrictive and non-realistic assumptions to find analytical solutions. Given these shortcomings, a comparison between structural and reduced-form models offers an interesting research area. Skiera (2010) has compared both models (to improve pricing decisions) and concluded that each has unique characteristics and offers promise for different areas of application. An even more profound analysis may lead to a better evaluation of the advantages of structural models compared with reduced-form equations. Finally, I emphasize the many opportunities to advance our knowledge in the interdisciplinary marketing discipline using theories developed in other sciences, such as economics and psychology. Even flashbacks to theories and models that were developed decades ago may be useful tools in this respect. Key takeaways:
1. Decision making in marketing benefits from knowledge that is based on specific research outcomes, generalized knowledge, and the development of models and methods. If decision making in marketing is based on such knowledge, it moves in the direction of distinguished marketing.
2. Generalized knowledge can be created by finding regularities, using panel data, conducting meta-analyses, and performing simulation experiments.
3. Early model building was based heavily on economic theory.
4. Marketing scientists should not always reinvent the wheel; they can use theories, methods, and techniques that have proven value in other disciplines.