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عنوان مقاله :

Application of metric conjoint analysis in family business research

ترجمه فارسی عنوان :

تجزیه و تحلیل متقارن متریک در پژوهش کسب و کار خانواده

سال انتشار : 2014

متعلق به مجله یا ژورنال : مجله استراتژی کسب و کار خانوادگی – Journal of Family Business Strategy

فرمت: PDF

تعداد صفحات: 13

شماره پروژه: 5120


کلمات کلیدی :

Metric conjoint analysis,Experimental research,Management research,Boundary research,Management consulting,Family business

تجزیه و تحلیل متقارن متریک، پژوهش تجربی، پژوهش مدیریت،پژوهش مرزی، مشاوردهی در امر مدیریت، کسب و کار خانواده


چکیده  :

Abstract

Strategic decisions are important because they influence the strategic direction, competitive positioning and performance of firms. However, these decisions are difficult to research, especially in family firms, as their top managers are even less forthcoming than those of their non-family counterparts. With the metric conjoint analysis method, researchers are able to analyze management decisions at the time when they are made. This method is based on an experimental technique in which the manager has to make a series of judgments based on a number of decision scenarios containing a set of decision attributes. Based on the experiment, the underlying structure of the manager’s decision-making process can be investigated. This article conducts a literature review of management studies using metric conjoint analysis, presents an exemplary study in the family business field and suggests potential future applications for this method in family business research.


مقدمه این مقاله :

 Introduction

One of the most central research areas in management and family business research is the assessment of firm performance (e.g., Davies, Chun, & Kamins, 2010; Surroca, Tribo´ , & Waddock, 2010; Yu, Lumpkin, Sorenson, & Brigham, 2012). Strategic decision making is one factor that influences firm performance (Hambrick, 1989; Priem & Harrison, 1994), and strategic decisions are thus an important source of competitive advantage (Penrose, 1959).Management and family business research studies strategic decisions by investigating research questions such as where are the organizational boundaries of a business (Alvesson & Lindkvist, 1993; Coase, 1937; Memili, Chrisman, Chua, Chang, & Kellermanns, 2011; Williamson, 1975) and what combination of resources do managers prefer to generate a sustainable competitive advantage (Barney, 1991; Chrisman, Chua, & Kellermanns, 2009; Penrose,1959; Rumelt, 1987; Sirmon, Arregle, Hitt, & Webb, 2008;Wernerfelt, 1984). Thus, the decisions of top managers are a cornerstone on which management research is built (Hutzschenreuter & Kleindienst, 2006).However, the cognitive processes underlying strategic decisions are neither directly measurable nor directly observable (Priem, Ndofor, & Voges, 2004). Thus, the analysis of these decisions often relies on post hoc research methods, such as questionnaires, surveys and interviews. However, these methods suffer from a number of shortcomings: Respondents may be affected by retrospective bias from misinterpreting what was responsible for an event or action (Golden, 1992). Biases and errors associated with self-reporting might occur (Podsakoff & Organ, 1986). Further, crucial information might be missing when respondents answer research questions after the event of interest has occurred (Phillips, 1981). Thus, research would benefit from additional research methods that overcome these limitations (Shepherd, 2011; Snow & Thomas, 1994).One of these methods is metric conjoint analysis (Louviere,1988). This method asks respondents to rate combinations of different levels of strategy variables in a hypothetical decision situation (Priem & Harrison, 1994) and thus allows researchers to study decisions and/or the results of decisions at the time when they are made (Lohrke, Holloway, & Woolley, 2010). Metric conjoint analysis is one of the various techniques that fall under the label ‘conjoint analysis’. Conjoint analysis techniques in general are widely used in research and practical applications,especially in marketing research and in commercial applications (Green & Srinivasan, 1990).


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