A spanning heuristic for the unordered and ordered matching identification problem

The matching identification problem (MIP) is a combinatoric search problem related to the fields of learning from examples, boolean functions, and knowledge acquisition. The MIP involves identifying a single “goal” item from a large set of items. Because there is commonly a cost associated with evaluating each guess, the goal item should be identified in as few guesses as possible. As in most search problems, the items have a similar structure, which allows an evaluation of each guessed item. In other words, each guessed item elicits partial information about the goal item, i.e. how similar the guess is to the goal. With this information the goal is more quickly identified.The unordered MIP has been studied by Mehrez and Steinberg (ORSA J. Comput. 7 (1995) 211) in which they proposed two different types of algorithms. The purpose of the present paper is to suggest an improved Spanning Heuristic algorithm. Its improvement increases as the problem size increases. Further results and comparisons are derived for the unordered and ordered cases.This research shows that when the search space is very large, it is better to inquire from items that are known not to be the goal (they have been ruled out by previous guesses), for the purpose of acquiring more information about the goal. As the search space is narrowed, it is better to guess items that have not been ruled out.     

The State of the Field in UK Management Research: Reflections of the Research Assessment Exercise (RAE) Panel

This paper reviews the state of the field of the sub-disciplines within UK management research, based upon the submissions of 94 UK higher education institutions to the Business and Management Studies Panel in the UK's 2001 Research Assessment Exercise (RAE). It offers observations on the UK model of the assessment of quality in, and funding of, research conducted in publicly funded higher education institutions.     

The Determinants of Trust and Credibility in Environmental Risk Communication: An Empirical Study

This study examines a key component of environmental risk communication; trust and credibility. The study was conducted in two parts. In the first part, six hypotheses regarding the perceptions and determinants of trust and credibility were tested against survey data. The hypotheses were supported by the data. The most important hypothesis was that perceptions of trust and credibility are dependent on three factors: perceptions of knowledge and expertise; perceptions of openness and honesty; and perceptions of concern and care. In the second part, models were constructed with perceptions of trust and credibility as the dependent variable. The goal was to examine the data for findings with direct policy implications. One such finding was that defying a negative stereotype is key to improving perceptions of trust and credibility.     

Editorial

Ohne Zusammenfassung     

One-dimensional optimal bounded-shape partitions for Schur convex sum objective functions

Consider the problem of partitioning n nonnegative numbers into p parts, where part i can be assigned n_i numbers with n_i lying in a given range. The goal is to maximize a Schur convex function F whose ith argument is the sum of numbers assigned to part i. The shape of a partition is the vector consisting of the sizes of its parts, further, a shape (without referring to a particular partition) is a vector of nonnegative integers (n₁,..., n_p) which sum to n. A partition is called size-consecutive if there is a ranking of the parts which is consistent with their sizes, and all elements in a higher-ranked part exceed all elements in the lower-ranked part. We demonstrate that one can restrict attention to size-consecutive partitions with shapes that are nonmajorized, we study these shapes, bound their numbers and develop algorithms to enumerate them. Our study extends the analysis of a previous paper by Hwang and Rothblum which discussed the above problem assuming the existence of a majorizing shape. This research is partially supported by ROC National Science grant NSC 92-2115-M-009-014.     

An exploratory study of manufacturing strategy practices of machinery manufacturing companies in India

This paper presents findings of a survey on manufacturing strategy practices adopted by the Indian machinery manufacturing companies (IMMC). Based on the survey, three companies have been selected for detailed case studies. Their experiences in the manufacturing strategy process are analyzed. Although the companies represented diversity in terms of product type, sales volume, and geographic location, they share several commonalties including use of advanced manufacturing technologies and use of several improvement techniques. The process of strategy formulation varied among the companies in terms of participants, complexity and degree of formalization. Competitive priorities, order winners and critical success factors are also identified for these companies. Based on strategic manufacturing issues, manufacturing competence index and business performance index for the companies has been worked out.     

Proximity and inter‐organizational collaboration: A literature review

The proximity concept is used in many different ways in the literature. These dimensions of proximity are, however, defined and measured in many different (sometimes even contradictory) ways, show large amounts of overlap, and often are under‐ or over‐specified. The goal of this paper is to specify the different dimensions of proximity relevant in inter‐organizational collaboration more precisely and to provide definitions of these dimensions. The research presented contributes to reducing the ambiguity of the proximity concept as used in the literature. Based on the above, the following research question is addressed in this paper: ‘Which dimensions of proximity are relevant in inter‐organizational collaboration and how are they defined?’ A systematic literature review is presented in order to disentangle the dimensions of the proximity concept. Based on this literature review, three dimensions of proximity relevant in inter‐organizational collaboration are distinguished: geographical proximity, organizational proximity and technological proximity. Examples (case studies) from the literature are used to illustrate the current conceptual ambiguity as well as to clarify how the proposed dimensions of proximity reduce this conceptual ambiguity.     

Explaining the Flight of Cupid’s Arrow: A Spatial Random Utility Model of Partner Choice

Spatial homogamy may be defined as follows: anyone may be attracted to anyone else, but near candidates are more attractive than distant candidates. In this article, we propose a model of partner choice, where homogamy is defined in terms of spatial, demographic, socioeconomic and cultural similarity. A spatial choice model using random utility theory is formulated, taking into account a relaxation of the independence from the irrelevant alternatives property, as spatial alternatives are not independent of one another. We model partner choice given the characteristics of the chosen partner and a choice set of alternatives, using unique micro data on all new cohabiters in the Netherlands, linked to other relevant data sets. The model takes the spatial locations of potential candidates within a choice set into account, including an indicator for the spatial similarity between alternatives. We find that spatial homogamy is a vital component of partner matching, aside from and adding to the spatial effects in demographic, socioeconomic and cultural homogamy. Given a choice set of partners, the highest likelihood of a match occurs with a person who is born and lives near by, who is close in age, is in the same life stage and has the same marital status, who has the same educational and income level and the same labour market status, who speaks the same dialect and lives in a culturally similar residential area. The distance effect is most pronounced for those individuals with lower levels of education and those living in rural areas.     

Bayesian adjustment for unidirectional misclassification in ordinal covariates

In this paper, we study the identification of Bayesian regression models, when an ordinal covariate is subject to unidirectional misclassification. Xia and Gustafson [Bayesian regression models adjusting for unidirectional covariate misclassification. Can J Stat. 2016;44(2):198–218] obtained model identifiability for non-binary regression models, when there is a binary covariate subject to unidirectional misclassification. In the current paper, we establish the moment identifiability of regression models for misclassified ordinal covariates with more than two categories, based on forms of observable moments. Computational studies are conducted that confirm the theoretical results. We apply the method to two datasets, one from the Medical Expenditure Panel Survey (MEPS), and the other from Translational Research Investigating Underlying Disparities in Acute Myocardial infarction Patients Health Status (TRIUMPH).