Assessment and selection of management consultants: A comparative cognitive study between small- and large-scale companies

This paper explores alternative ways of evaluating and ultimately hiring a generalist consultancy for short- and medium-term projects, and how this is shaped by the size of the clients’ company. What buying factors do managers of small and large firms have in mind when hiring consultants for their projects?Instead of employing surveys incorporating predefined buying factors based on what the researchers think would be important, a cognitive mapping methodology - the repertory grid technique - is applied to investigate the underlying phenomena. Using the repertory grid technique to draw mental models is a novel research technique within the supply management field. Recent behavioral supply chain management research found the behavior of managers to be critically influenced by mental models: the lenses through which managers perceive, simplify and interpret the world.Results show differences between the buying factors used in small- and large-scale companies: managers of large companies are more ‘reputation and outcome-oriented’ and managers of small businesses are more concerned with ‘how the services are rendered’. However, both constituents stressed the importance of expertise, quality and the relationship between the client and the management consultancy, although the character of such a relationship differed between small- and large-scale firms. Results also point out criteria that managers of small and large companies employ to discriminate between good and poor performing consultancies and to judge service delivery. The practical implications for both companies hiring consultancies and the consultancy companies are discussed. For consultancies, our findings can help tailoring their efforts to a differing clientele when marketing and selling their services. For companies hiring consultancies, fixating too much on particular buying factors may lead to biased decisions.     

Information processing and innovative choice

In this paper we examine how the information processing of subjects who make an innovative choice (innovators) differs from that of subjects who make a noninnovative choice (noninnovators). The task involves selection of an alternative within a range of prerated product category innovativeness. We propose that subjects who seek 1) impersonal/uncontrollable sources, 2) higher levels of information, 3) more detailed (versus summary) data, and 4) noncomparative (versus comparative) data are more likely to make innovative choices. The research method is a computerized process tracing experiment utilizing Search Monitor (Brucks 1988).The authors wish to express their gratitude to Merrie Brucks for the use of and help with Search Monitor and to Eric Johnson, Dave Schmittlein, and Mita Sujan for helpful comments on an earlier version of this paper.     

Economic performance and “frontier” efficiency: A product life-cycle approach

We propose to introduce some economic performance variables of the firms and relate these with the corresponding frontier efficiency measures of 13 industries of the Belgian manufacturing sector in 1978. In order to find groups of similar firms by applying a cluster technique, we assume that this relationship is of a product life cycle nature.In the light of the product life cycle scheme itself and variations of it we can consider the possible explanatory variables of technical inefficiency in each group of firms as sources which intensify or slow-down the economic performance of the firm under consideration. Some cautious characterization of the top and bottom level clusters is formulated and linked to the efficiency issue.This paper was processed by N. R. Adam     

Deregulation,news releases,and price discovery

We hypothesize that the firm’s regulatory environment influences the sensitivity of its equity value to information. Using intraday stock price data of utilities operating in differing regulatory environments we test for systematic differences between the responsiveness of stock prices of utility firms operating in deregulated and regulated environments to a common information set. Our findings reveal sharp differences in responses, with those of utilities operating in deregulated environments the strongest, and the responses of utilities in highly regulated environments the weakest. While the evidence supports our hypothesis, in a broader sense, the evidence suggests that deregulation aids in the process of price discovery. We also find evidence that suggests that deregulation, per se, does not lead to higher stock price volatility.      

Nonlinearities and heterogeneity in environmental quality: An empirical analysis of deforestation

We use a panel data set of 59 developing countries over the 1972–1994 period to study the deforestation process. Relying on both parametric and semiparametric models, we examine nonlinearities and heterogeneity in the deforestation process. We first study the existence of an Environmental Kuznets Curve (EKC) and then analyze determinants of deforestation. Our data sample provides no evidence of an EKC. We also find that political institution failures may worsen the deforestation process in developing countries.     

The Middle Class Consensus and Economic Development

A middle class consensus is defined as a high share of income for the middle class and a low degree of ethnic divisons. The paper links a middle class consensus to resource endowments, along the lines of the provocative thesis of Engerman and Sokoloff (1997 and 2000). This paper exploits this association using tropical resource endowments as instruments for inequality. A higher share of income for the middle class and lower ethnic divisions are associated with higher income and higher growth, as well as with more education, better health, better infrastructure, better economic policies, less political instability, less civil war and ethnic minorities at risk, more social modernization and more democracy.     

Convenient estimators for the panel probit model: Further results

Bertschek and Lechner (1998) propose several variants of a GMM estimator based on the period specific regression functions for the panel probit model. The analysis is motivated by the complexity of maximum likelihood estimation and the possibly excessive amount of time involved in maximum simulated likelihood estimation. But, for applications of the size considered in their study, full likelihood estimation is actually straightforward, and resort to GMM estimation for convenience is unnecessary. In this note, we reconsider maximum likelihood based estimation of their panel probit model then examine some extensions which can exploit the heterogeneity contained in their panel data set. Empirical results are obtained using the data set employed in the earlier study. Helpful comments and suggestions by Irene Bertschek and Michael Lechner are gratefully acknowledged. This paper has also benefited from comments by two anonymous referees and from seminar participants at the Center for Health Economics at the University of York. Any remaining errors are the responsibility of the author.