مقاله انگلیسی رایگان در مورد پیش بینی نتایج در بازاریابی تجارت به تجارت – امرالد ۲۰۱۸

emerald

 

مشخصات مقاله
ترجمه عنوان مقاله پیش بینی عینی نتایج دقیق در بازاریابی تجارت به تجارت
عنوان انگلیسی مقاله Accurately Predicting Precise Outcomes in Business-to-Business Marketing
انتشار مقاله سال ۲۰۱۸
تعداد صفحات مقاله انگلیسی ۲۳ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه امرالد
نوع نگارش مقاله
مقالات فصلی (Chapter Item)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) scopus
فرمت مقاله انگلیسی  PDF
شاخص H_index ۷ (۲۰۱۸)
شاخص SJR ۰٫۱۱۶ (۲۰۱۸)
رشته های مرتبط مدیریت
گرایش های مرتبط بازاریابی، مدیریت کسب و کار
نوع ارائه مقاله
ژورنال
مجله / کنفرانس بهبود اتحاد و مدل سازی و نظریه برای پیش بینی دقیق نتایج – Improving the Marriage of Modeling and Theory for Accurate Forecasts of Outcomes
کلمات کلیدی دقت؛ بازاریابی صنعتی؛ مدیریت؛ مدل سازی؛ پیش بینی؛ علوم پایه
کلمات کلیدی انگلیسی Accuracy; industrial marketing; management; modeling; prediction; science
شناسه دیجیتال – doi
https://doi.org/10.1108/S1069-096420180000025006
کد محصول E9369
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
INTRODUCTION: ACHIEVING SCIENTIFIC LEGITIMACY
PREDICTING WHAT  DIRECTIONS OR OUTCOMES?
PREDICTING PRECISE OUTCOMES IN THE B-TO-B LITERATURE
EMBRACING COMPLEXITY THEORY AS THE FOUNDATIONAL PHILOSOPHY IN B-TO-B RESEARCH
CONCLUDING REMARKS
References

بخشی از متن مقاله:

INTRODUCTION: ACHIEVING SCIENTIFIC LEGITIMACY

LaPlaca and colleagues (Hadjikhani & LaPlaca, 2013; LaPlaca, 1997; LaPlaca & da Silva, 2016) described in-depth the first paradigm shift in business-to-business (B-to-B) research from description and explanation of business exchanges based on transactions to description and explanation of business exchanges based on relationships. Equally important, they identify what is still necessary to accomplish for B-to-B research to achieve scientific legitimacy, “B2B relationships as a subject of scientific enquiry will need to seriously engage into what can be termed a true paradigm shift, one that advances discovery in this area from sheer descriptive analysis and reporting to the development of explanatory schemata and theoretical frameworks of a kind that allow for more accurate prediction of underlying B2B phenomena” (LaPlaca & da Silva, 2016, p. 232). LaPlaca and colleagues provide foundation insights into the steps necessary to be taken to achieve scientific legitimacy, including embracing prediction and control as necessary objectives in B-to-B research  research focusing on description and explanation is necessary but insufficient for advancing science in the B-to-B discipline. “In conducting scientific investigations, researchers, particularly scientists studying physical phenomena, progress through a hierarchy of types of research: descriptive, explanatory, predictive, and control (LaPlaca, 2013). The ultimate goal of science is to control events where possible … Improved understanding and predictive capabilities will reduce marketing errors and improve overall marketing effectiveness and efficiency. In this way, B-to-B marketing research will truly make a contribution to society” (LaPlaca & da Silva, 2016, p. 232). The following discussion focuses on how to accomplish the true paradigm shift that LaPlaca and colleagues identify. The study here provides examples of research contributing to knowledge and theory that advance prediction and control in B-to-B contexts. The study indicates that shifting beyond linear model construction and symmetric tests (i.e., multiple regression analysis (MRA) and structural equation modeling (SEM)) and embracing complexity theory and asymmetric tests (i.e., constructing and testing algorithms by “computing with words,” Zadeh, 1996, 2010) are necessary steps to be taken to accomplish the true paradigm shift. Researchers in B-to-B research benefit from recognizing that the current dominant logic of performing null hypothesis testing (NHST via MRA and SEM) is “corrupt research” (Hubbard, 2016) practice and from recognizing that predicting by algorithms via somewhat precise outcome testing (SPOT) advances B-to-B research toward achieving scientific legitimacy. Following this introduction, the second section answers the question, “Predicting what  directions or outcomes?” The third section provides examples of predicting precise outcomes in the B-to-B research literature.

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