Vanderheyden, Karlien; Cools, Eva; De Baets, Shari (2011)
Questionnaires using Likert-type rating scales are an important source of data in marketing research. Researchers use different rating scale formats with varying numbers of response categories and varying label formats (e.g., 7-point rating scales labeled at the endpoints, fully labeled 5-point scales, etc.) but have few guidelines when selecting a specific format. Drawing from the literature on response styles, we formulate hypotheses on the effect of the labeling of response categories and the number of response categories on the net acquiescence response style, extreme response style and misresponse to reversed items. We test the hypotheses in an online survey (N = 1207) with eight experimental conditions and a follow-up study with two experimental conditions (N = 226). We find evidence of strong effects of scale format on response distributions and misresponse to reversed items, and we formulate recommendations on the choice of a scale format.
The purpose of this paper is to model analysts’ forecasts. The paper differs from the previous research in that we do not focus on how accurate these predictions may be. Accuracy may indeed be an important quality but we argue instead that another equally important aspect of the analysts’ job is to predict and describe the impact of jump events. In effect, the analysts’ role is one of scenario prediction. Using a Bayesian-inspired generalised method of moments estimation procedure, we use this notion of scenario prediction combined with the structure of the Morgan Stanley analysts’ forecasting database to model normal (base), optimistic (bull) and pessimistic (bear) forecast scenarios for a set of reports from Asia (excluding Japan) for 2007–2008. Since the estimation procedure is unique to this paper, a rigorous derivation of the asymptotic properties of the resulting estimator is also provided.
Van den Broeck, Herman; De Pauw, Ann-Sophie; Wit, Arjaan (2013)
This paper examines the impact of perceived unethical behavior by entrepreneurs, angel investors and venture capitalists on their conflict process. For this purpose, we use an embedded case study design to provide a diversity of perspectives on the topic at hand. From the eye of the beholder, i.e. investor, entrepreneur or both, 11 conflict situations were analyzed for any perceived unethical behavior. Based on findings from within- and cross-case analysis, we propose that perceived unethical behavior among venture partners triggers conflicts between them through increased fault attribution or blaming. Further, we propose that perceived unethical behavior affects venture partners’ choice of conflict management strategy and increases the likelihood of conflict escalation and of conflict having a negative partnership outcome such as failure or another form of involuntary exit. As such, this paper contributes to the entrepreneurship literature by addressing calls for more research on the darker sides of investor–investee relationships.
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