مقاله انگلیسی رایگان در مورد چگونه جمع سپاری پیش بینی نتایج بازار گرا را ارتقا می بخشد ( الزویر )

مقاله انگلیسی رایگان در مورد چگونه جمع سپاری پیش بینی نتایج بازار گرا را ارتقا می بخشد ( الزویر )

 

مشخصات مقاله
عنوان مقاله  How crowdsourcing improves prediction of market-oriented outcomes
ترجمه عنوان مقاله  چگونه جمع سپاری پیش بینی نتایج بازار محور را بهبود می بخشد
فرمت مقاله  PDF
نوع مقاله  ISI
سال انتشار

مقاله سال ۲۰۱۶

تعداد صفحات مقاله  ۹  صفحه
رشته های مرتبط  اقتصاد
گرایش های مرتبط  اقتصاد پولی و اقتصاد مالی
مجله  مجله تحقیقات بازاریابی – Journal of Business Research
دانشگاه Saint Joseph’s University, USA
کلمات کلیدی  جمع آوری اطلاعات، پردازش اطلاعات بازار، پیش بینی بازار، پیش بینی، یادگیری سازمانی
کد محصول  E5119
نشریه  نشریه الزویر
لینک مقاله در سایت مرجع  لینک این مقاله در سایت الزویر (ساینس دایرکت) Sciencedirect – Elsevier
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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بخشی از متن مقاله:
Introduction

Managers must continually respond to a rapidly changing marketplace in order to protect their competitive advantage. This obligation is recognized across diverse disciplines such as marketing (Day, 2011), innovation (Bharadwaj & Noble, 2015), strategic management (D’Aveni, Dagnino, & Smith, 2010), and forecasting (Armstrong, 2006). Dynamic capabilities, a firm’s ability to adapt competences and resources to better respond to the marketplace (Eisenhardt & Martin, 2000; Teece, Pisano, & Shuen, 1997), play an important role in this regard. As business leaders manage this evolution, one of the most important and difficult tasks they undertake is to predict uncertain market conditions and future business outcomes (Day, 2011; Yan & Ghose, 2010). The ability to make effective predictions about markets is especially important as managers develop strategies and implement plans, and forecast the resulting impact on sales, profit, and firm value (Morgan, 2012; Rao & Bharadwaj, 2008). This article introduces crowdsourcing as an innovative decision support tool that can enhance market information processing and directly improve market-oriented predictions (e.g., consumer preferences or competitor response) and business forecasts (e.g., sales or profits) (Narver & Slater, 1990).

These market-oriented predictions occur at the intersection of important dynamic capabilities: market learning is translated into forecasts that support planning and implementation, new product development, pricing, and strategic decision-making (Morgan, 2012; Vorhies & Morgan, 2005). Market learning not only plays a critical role at this intersection (Cepeda & Vera, 2007) but it is also identified as one of the most significant areas of dynamic and marketing capabilities improvement (Morgan, 2012; Vorhies & Morgan, 2005; Vorhies, Orr, & Bush, 2011).

Unfortunately, forecasting often struggles to be proficient in forecasting (Kahn, 2002; Srivastava, Shervani, & Fahey, 1999; Yan & Ghose, 2010). For example, in response to a major product launch failure, attributed largely to forecasting errors, Procter & Gamble’s CEO pledged to make better use of online tools to support demand forecasting and innovation initiatives (Neff, 2012). As an example of the importance of forecasting in designing and executing business strategies, the Marketing Science Institute designates providing guidance to firms on forecasting as one of its foremost 2014–۲۰۱۶ research priorities (MSI, 2014). These examples raise the question of how firms can improve market-oriented predictions and forecasts.

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