Aggregated Causal Maps: An Approach To Elicit And Aggregate The Knowledge Of Multiple Experts

This paper presents a systematic procedure to elicit and aggregate the knowledge of multiple individual experts and represent it in the form of an Aggregated Causal Map (ACM). This procedure differs from existing methods in two ways. First, unlike other methods, this method does not rely on group interaction in eliciting knowledge of multiple experts, and, therefore, is not fraught with biases associated with group dynamics. Second, this method uses both the idiographic and nomothetic approaches while existing methods focus on nomothetic approaches to knowledge elicitation. We draw on the strengths of both approaches by using the idiographic approach to elicit and aggregate the knowledge of multiple experts and the nomothetic approach to validate the knowledge elicited. We illustrate the procedure by constructing the ACM of eight key decision makers about an enterprise system adoption decision

Mindsets and Internationalization Success: An Exploratory Study of the British Retail Grocery Industry

Despite the growing pressures of internationalization, failures in international efforts are becoming increasingly widespread. Previous literature has developed external environmental and internal firm-specific explanations of international success, but has ignored the role of mindsets in understanding international failures. This gap is especially important because recent studies contend that the mindsets or the way top managers make sense of their global environment is central to international decision making and outcomes. We propose that mindsets are important in explaining international success. We compare the mindsets of two matched firms in the British retail grocery industry—one successful (Tesco) and another unsuccessful (Sainsbury)—from 1988 to 2003. Our results suggest systematic differences between the successful (Tesco) and the unsuccessful (Sainsbury) firm in two specific facets of mindsets—complexity and reactivity. These results, although exploratory, do highlight the importance of mindsets in the process of internationalization and raise interesting questions for future empirical examination

A Causal Mapping Approach to Constructing Bayesian Networks

This paper describes a systematic procedure for constructing Bayesian networks (BNs) from domain knowledge of experts using the causal mapping approach. We outline how causal knowledge of experts can be represented as causal maps, and how the graphical structure of causal maps can be modified to construct Bayes nets. Probability encoding techniques can be used to assess the numerical parameters of the resulting Bayes nets. We illustrate the construction of a Bayes net starting from a causal map of a systems analyst in the context of an information technology application outsourcing decision.The research has been supported by two grants from the Kansas University Business School PhD Summer Research Fund to both authors and by a grant from the Kansas University General Research Fund to Prakash P. Shenoy

A Bayesian Network Approach to Making Inferences in Causal Maps

The main goal of this paper is to describe a new graphical structure called "Bayesian causal maps" to represent and analyze domain knowledge of experts. A Bayesian causal map is a causal map, i.e., a network-based representation of an expert's cognition. It is also a Bayesian network, i.e., a graphical representation of an expert's knowledge based on probability theory. Bayesian causal maps enhance the capabilities of causal maps in many ways. We describe how the textual analysis procedure for constructing causal maps can be modi®ed to construct Bayesian causal maps, and we illustrate it using a causal map of a marketing expert in the context of a product development decision.The research was supported by two University of Kansas School of Business PhD Summer Research Fund grants to both authors and by one Kansas University General Research Fund grant to Prakash P. Shenoy

Validity of the structural properties of text-based causal maps: An empirical assessment

Organizational Research Methods, 8(1): pp. 9-40.Recently, text-based causal maps (TBCMs) have generated enthusiasm as a methodological tool because they provide a way of accessing large, untapped sources of data generated by organizations. Although TBCMs have been used extensively in organizational behavior and strategic management research, studies assessing the psychometric properties of TBCM measures are virtually non-existent. With the intention of facilitating large sample substantive research using TBCMs, we examine the construct validity of two most frequently employed structural properties of TBCMs: complexity and centrality. In assessing construct validity, we examine the internal consistency, dimensionality and predictive validity of the structural properties. Our results suggest that complexity is not a general cognitive attribute. Rather, it is indicative of domain knowledge. On the other hand, centrality, which reflects the degree of hierarchy characterizing the TBCM, is related to cognitive ability, and may reflect general information processing. Moreover, we found that complexity and centrality, but not cognitive ability, predicted student performance. We discuss the implications of these results

CEO succession and the CEO’s commitment to the status quo

Chief executive officer (CEO) commitment to the status quo (CSQ) is expected to play an important role in any firm’s strategic adaptation. CSQ is used often as an explanation for strategic change occurring after CEO succession: new CEOs are expected to reveal a lower CSQ than established CEOs. Although widely accepted in the literature, this relationship remains imputed but unobserved. We address this research gap and analyze whether new CEOs reveal lower CSQ than established CEOs. By analyzing the letters to the shareholders of German HDAX firms, we find empirical support for our hypothesis of a lower CSQ of newly appointed CEOs compared to established CEOs. However, our detailed analyses provide a differentiated picture. We find support for a lower CSQ of successors after a forced CEO turnover compared to successors after a voluntary turnover, which indicates an influence of the mandate for change on the CEO’s CSQ. However, against the widespread assumption, we do not find support for a lower CSQ of outside successors compared to inside successors, which calls for deeper analyses of the insiderness of new CEOs. Further, our supplementary analyses propose a revised tenure effect: the widely assumed relationship of an increase in CSQ when CEO tenure increases might be driven mainly by the event of CEO succession and may not universally and continuously increase over time, pointing to a “window of opportunity” to initiate strategic change shortly after the succession event. By analyzing the relationship between CEO succession and CEO CSQ, our results contribute to the CSQ literature and provide fruitful impulses for the CEO succession literature

The Tale of Two Cultures: Attitudes towards Affirmative Action in the United States and India

This study seeks to inform multinational corporations as they integrate domestic and international affirmative action policies and strategies. Improvement of these abilities can have important implications for human resource management and organizational productivity outcomes. To increase our understanding of the international perspectives of affirmative action, we examine employee perceptions of the structure of affirmative action plans in the United States and India. The differences in affirmative action plans implemented in these countries as well as country cultural differences offer interesting backdrops for examining cross-country differences in employee perceptions of affirmative action

Biographies

This paper describes a systematic procedure for constructing Bayesian networks from domain knowledge of experts using the causal mapping approach. We outline how causal knowledge of experts can be represented as causal maps, and how the graphical structure of causal maps can be modified to construct Bayes nets. Probability encoding techniques can be used to assess the numerical parameters of the resulting Bayes nets. We illustrate the construction of a Bayes net starting from a causal map of a systems analyst in the context of an information technology application outsourcing decision

Implementing Affirmative Action Plans in Multinational Corporations

Diversity is rapidly becoming an imperative business strategy in the global economy. Affirmative action is central to promoting a positive diversity climate. However, cultural, social, and legal differences around the world pose major challenges to multinational corporations (MNCs) in developing successful affirmative action plans (AAPs) at the global level. We present a model for global affirmative action plans that first identifies and examines the major environmental challenges faced by MNCs in implementing global affirmative action practices, as well as suggestions to combat these challenges. Second, the model delineates global affirmative action practices that rest on two building blocks—differentiation and integration. On the one hand, multinationals need to recognize the cultural, social, and legal differences between different countries around the world and customize affirmative action policies to each geographic context. On the other hand, they need to develop integration mechanisms to connect different affirmative action plans around the world by promoting information sharing and synergies. By effectively balancing differentiation and integration, MNCs can develop a “cosmopolitan mindset” of affirmative action that will enhance effectiveness in a global environment