Market anticipation of merger activities: An empirical test

This paper empirically examines the possibility that there is leakage of information regarding a merger prior to the announcement of the first bid for the target firm. The tests for the existence of market anticipation are based on the behavior of variances implied in the premia of call options listed on the target firms' stocks. We conclude that the evidence is consistent with the hypothesis that the market anticipates an acquisition prior to the first announcement.     

Stock authorizations: A sequential equilibrium analysis

We develop a sequential equilibrium model of the common stock authorization process. We provide conditions under which actions that increase the number of slack shares, such as stock authorizations, generate negative announcement effects.     

Transformative change towards sustainability: the interaction between organisational discourses and organisational practices

This paper adopts a case study approach to explore the complex process of organisational change towards greater social and environmental sustainability. The case study of a major global financial services organisation involved interviews and examination of company documents, and their website over the period 2000–2014. The rare longitudinal empirical evidence from different sources provides important insights to how companies are responding to increasing demands for sustainable development. Using Laughlin’s [1991. Environmental disturbances and organizational transitions and transformations: some alternative models. Organization Studies, 12 (2), 209–232] pathways of change model, the study investigates the interaction between organisational discourses (i.e. its interpretive schemes) and organisational practices (i.e. design archetypes). The findings demonstrate the centrality of organisational discourses, especially those relating to accounting calculative practices, to radical change towards sustainable development. The paper also contributes to the literature on institutional logics, particularly multiple institutional logics, and how these are implicated in change processes.     

Relay network design in freight transportation systems

This paper provides techniques, heuristics, and algorithms for the location of a minimal number of relay points on a highway network that satisfies the driver-distance constraint––a driver leaving a distribution center does not travel for more than t miles before which he/she returns back to the originating point or rests before moving on further. Straight Route and Detour versions are considered. Empirical evaluations of the proposed heuristics on road networks are performed. The results indicate that the Straight Route version is computationally efficient but locates a larger number of relay points than the Detour versions.     

Assessing sales contest effectiveness: the role of salesperson and sales district characteristics

Sales contests are widely employed to improve short-term sales performance, but knowledge about their effectiveness at the individual salesperson level remains sparse. Proponents argue that contests increase sales by stimulating salespeople, while critics say that contests merely encourage strategic timing of sales efforts. The authors draw on the strategic sales timing literature and goal theory to hypothesize that in a consultative selling scenario, sales will dip below the baseline before the contest but increase above the baseline during and after the contest. They posit that sales district potential and salesperson ability moderate the pre-contest sales dip, contest sales boost, and post-contest sales. Results from a model based on individual-level data on 1180 salespeople in 78 sales districts are largely supportive of the hypotheses. They highlight the need for researchers to integrate the role of strategic timing, salesperson, and sales district characteristics to assess sales contest outcomes. For practitioners, the findings show that in consultative selling situations, contests can generate a net sales increase despite the occurrence of timing games, and the sales gain is higher in districts with lower sales potential and among salespeople with higher sales ability.     

An empirical analysis of individual level casino gambling behavior

Gambling and gaming is a very large industry in the United States with about one-third of all adults participating in it on a regular basis. Using novel and unique behavioral data from a panel of casino gamblers, this paper investigates three aspects of consumer behavior in this domain. The first is that consumers are addicted to gambling, the second that they act on “irrational” beliefs, and the third that they are influenced by marketing activity that attempts to influence their gambling behavior. We use the interrelated consumer decisions to play (gamble) and the amount bet in a casino setting to focus on addiction using the standard economic definition of addiction. We test for two irrational behaviors, the “gambler’s fallacy” and the “hot hand myth”—our research represents the first test for these behaviors using disaggregate data in a real (as opposed to a laboratory) setting. Finally, we look at the effect of marketing instruments on the both the decision to play and the amount bet. Using hierarchical Bayesian methods to pin down individual-level parameters, we find that about 8% of the consumers in our sample can be classified as addicted. We find support in our data for the gambler’s fallacy, but not for the hot hand myth. We find that marketing instruments positively affect gambling behavior, and that consumers who are more addicted are also affected by marketing to a greater extent. Specifically, the long-run marketing response is about twice as high for the more addicted consumers.     

Marketing modeling reality and the realities of marketing modeling

This paper shows how analytic modeling research in the Marketing field is focused on answering questions of “How?” and “Why?” It describes the disciplines involved in analytic modeling; examines how the key criteria of parsimony and robustness help to define a good model; and discusses other goodness criteria, including appropriate use of analytic techniques, applicability of the model to institutionally rich, real-world problems, non-obvious results, generalizability, and ability to provide insight where other research techniques do not work. The paper defines and discusses key concepts in analytic models of distribution channels, including double marginalization, coordination, incentive alignment and contract design, strategic substitutability and complementarity, externalities, and principal–agent problems. Next, the paper summarizes research presented in the session on analytic models in channels at the Erin Anderson conference; and finally, the paper suggests avenues for future analytic modeling research.     

Brand related information as context: the impact of brand name characteristics on memory and choice

Consumer exposure to new brand names can occur in contexts with or without brand information being present. Whereas previous research has examined the effects of brand name characteristics (association set size and word frequency) on memory in the presence of brand information, this paper also assesses brand name effects in contexts without brand related information and extends it to brand consideration and choice. Two different processes are found to be operating as consumers retrieve brands and make a choice. Recall seems to benefit from a distinctiveness based route, which also guides consideration and choice. In contrast, implicit memory is influenced by familiarity, with corresponding consideration and choice effects. The implications are that firms’ choice of brand names and media needs to consider the combination of brand name characteristics that will lead to desired outcomes vis-à-vis distinctiveness or familiarity based processes.     

Risk assessment in internal auditing: a neural network approach

Risk assessment is a systematic process for integrating professional judgments about relevant risk factors, their relative significance and probable adverse conditions and/or events leading to identification of auditable activities (IIA, 1995, SIAS No. 9). Internal auditors utilize risk measures to allocate critical audit resources to compliance, operational, or financial activities within the organization (Colbert, 1995). In information rich environments, risk assessment involves recognizing patterns in the data, such as complex data anomalies and discrepancies, that perhaps conceal one or more error or hazard conditions (e.g. Coakley and Brown, 1996; Bedard and Biggs, 1991; Libby, 1985). This research investigates whether neural networks can help enhance auditors’ risk assessments. Neural networks, an emerging artificial intelligence technology, are a powerful non‐linear optimization and pattern recognition tool (Haykin, 1994; Bishop, 1995). Several successful, real‐world business neural network application decision aids have already been built (Burger and Traver, 1996). Neural network modeling may prove invaluable in directing internal auditor attention to those aspects of financial, operating, and compliance data most informative of high‐risk audit areas, thus enhancing audit efficiency and effectiveness. This paper defines risk in an internal auditing context, describes contemporary approaches to performing risk assessments, provides an overview of the backpropagation neural network architecture, outlines the methodology adopted for conducting this research project including a Delphi study and comparison with statistical approaches, and presents preliminary results, which indicate that internal auditors could benefit from using neural network technology for assessing risk. Copyright © 1999 John Wiley & Sons, Ltd.