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Bad to Good

Achieving High Quality and Impact in Your Research

By (author) Arch G. Woodside
Format: Paperback / softback
Publisher: Emerald Publishing Limited, Bingley, United Kingdom
Imprint: Emerald Group Publishing Limited
Published: 25th Aug 2016
Dimensions: w 152mm h 229mm d 17mm
Weight: 431g
ISBN-10: 1786353342
ISBN-13: 9781786353344
Barcode No: 9781786353344
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Synopsis
For decades, scholars have bemoaned the low relevancy and impact of most research in the leading journals in business, management, and marketing. The majority of the research that gets published, perhaps 70% of it, hardly has any measurable scholarly impact in terms of citations. Rather than low relevancy, 'Bad to Good' posits that the deeper issue is the pervasive use of bad research practices appearing in most articles in almost all ranked journals in the sub-disciplines of business. With the objective of reducing the high volume of bad practices in research in finance, management and marketing, the book offers tools for improving theory construction and empirical testing of theory especially by early-to-mid scholars. 'Bad to Good' covers 24 common bad practices, explaining why they are bad and how to replace them with good practices. Arch Woodside is a leading voice on how to improve business research. He served as the Editor-in-Chief of the 'Journal of Business Research' (JBR) for forty years. In 2016 the JBR ranked first among the top-twenty journals in marketing in the Google.com/scholar h-5 index (an impact metric) and seventh among the strategic management sub-discipline.

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Business scholars describe the bad research practices that are taught in courses and promulgated in most articles in most scholarly journals of finance, management, marketing, and organizational studies. They cover moving away from bad practices in research toward constructing useful theory and doing useful research; embrace complexity theory, perform contrarian case analysis, and model multiple realities; moving beyond multiple regression analysis and symmetric test to algorithms and asymmetric tests; case-based modeling of business-business relationships; performing triple sensemaking in field experiments; complexity theory, configural analysis, and deepening the service dominant logic; and complexity theory and human resources management: transcending variable and case-based perspective of service employees' (un)happiness and work performance. -- Annotation * (protoview.com) *