The Internal Validity of the Trade-Off Method of Conjoint Analysis

The discovery of consumers' preferences for multiattribute products/services has recently become possible with the development of conjoint measurement techniques. Conjoint analysis uses an individual's preference ranking or rating of some deliberately manipulated constructs of a concept to determine his/her numerically valued preferences for levels of those attributes that comprise the concept. Generally speaking, there are two methods of data collection for conjoint analysis: the full profile and the trade-off methods. This paper calls into question the internal validity of the preferences obtained via the trade-off method. An empirical analysis, which is based on the subjects' preferences for selecting a hospital, clearly shows that the estimates derived from the trade-off method may not reflect consumers' true preferences.     

Applications and Implementation DETERMINING THE REWARDS SALESPEOPLE VALUE: A COMPARISON OF METHODS*

Although salesmanagers can attempt to motivate salespeople by manipulating their rewards, these manipulations will fail if salespeople do not value the rewards and their levels. Methods for determining valued rewards can therefore be important to those charged with their determination. This study compares three measurement schemes for assessing salespeople's valences for various rewards: functional measurement, conjoint analysis, and thermometer scaling. All three schemes produce similar conclusions as to how rewards are valued and the acceptable tradeoffs among them. Thermometer scaling outperformed the others with respect to the time it took to secure the judgments and the palatability of the task, while conjoint measurement performed least well in these respects.     

Effect of Level of Disaggregation on Conjoint Cross Validations: Some Comparative Findings*

Early formulations of conjoint models focused on part-worth estimation at the individual level. As the methodology's popularity grew so did industry demands for increasingly larger numbers of attributes and levels. In response to these demands, new approaches, based on partial or full data aggregation (such as clusterwise/latent class conjoint and choice-based conjoint), have appeared. This paper suggests that pooled-data models will often be successful in predicting market shares when researchers employ monotonic attributes. In these cases more of a good attribute (or less of a bad attribute) is always more preferred. In the more realistic case, in which some of the attributes may be nonmonotonic, we find that data aggregation does not predict holdout sample preferences as well as individual part-worth models.     

Ensuring units invariant slack selection in radial data envelopment analysis models,and incorporating slacks into an overall efficiency score

This paper introduces a new methodology ensuring units invariant slack selection in radial DEA models and incorporating the slacks into an overall efficiency score. The CCR and BCC models are units invariant in their radial component, but not in their slack component, thus changing the units of measurement of one or more variables can change the models' solution. The proposed Full Proportional Slack (FPS) methodology improves the slack selections of the CCR and BCC models by producing slack selections that (a) are units invariant, thus producing fully units invariant models, (b) maximize the relative improvements represented by the slacks, and not their values, and (c) measure the full slacks that need to be removed from their corresponding variables. The FPS methodology is a fully oriented methodology first maximizing the improvements in the variables on the side of the orientation of the model. The Proportional Slack Adjusted (PSA) methodology incorporates the FPS slacks into an overall efficiency score, making it easier to interpret and use the results. The FPS and PSA methodologies are illustrated using an input oriented VRS Loan Quality DEA model with data from the retail branch network of one of Canada's largest banks.     

Behavioral complexity in leadership: The psychometric properties of a new instrument to measure behavioral repertoire

Research in managerial and executive leadership recognizes the importance of behavioral complexity, particularly for addressing the competing demands and roles expected of managerial leaders. Though some empirical research on behavioral complexity exists, further progress requires a more rigorous instrument to measure behavioral repertoire. We design an elaborated, multi-dimensional instrument based on the Competing Values Framework (CVF). To examine the underlying conceptual structure and remove measurement error, we test this second-order measurement model using structural equation modeling (SEM). We also test the spatial relationship of the factors using a Bayesian circumplex model. Our data largely support the theoretical structure and stringent demands of the CVF model as applied to this instrument. Finally, we test the instrument's ability to predict managerial effectiveness and find that higher overall ability is correlated with greater overall performance. With this new instrument, we suggest the modification and addition of roles associated with the CVF model.     

REPLY TO: “ON THE SENSITIVITY OF BRAND-CHOICE SIMULATIONS TO ATTRIBUTE IMPORTANCE WEIGHTS”

Our reply to Curry, Louviere, and Augustine's critique of our earlier paper focuses on differences in motivation between our research and theirs. Our interest in the problem relates to the possible incorporation of self-explicated evaluations in conjoint data collection methods; subsequent to the appearance of our original paper, we have developed hybrid models that combine elements of self-explicated (compositional) and conjoint (decompositional) data collection procedures. As far as we can surmise from their critique, Curry, Louviere, and Augustine are concerned with much broader strategic issues relating share of choices in the consumer population to changes in the shape of attribute weight distributions, shape of the Pareto tradeoff boundary, and so on.     

Analysis and Approximation of a JIT Production Line: A Comment

A recent Decision Sciences article by Jordan [9] presented a Markov-chain model of a just-in-time (JIT) production line. This model was used to estimate average inventories and production rates to find the optimal number of kanbans. Results for expected production rate were found to be consistently lower than those obtained by Huang, Rees, and Taylor [8] in a previous Decision Sciences article. Jordan attributed this unexpected outcome to some procedural problems in Huang et al.'s simulation methodology. In this paper, Markov-numerical analysis is used to compare the performance of Jordan's and Huang et al.'s methods of production control. Simulation analysis is then used to determine the effects of finite withdrawal cycle times. Results show that, for equal numbers of kanbans, Huang et al.'s two-card method of production control provides substantially greater expected production rates than Jordan's method. These results suggest that the Jordan model should not be applied to the problem of setting kanban numbers on manual JIT lines. Finally, we comment on the efficiency of Jordan's iterative method to obtain performance measures of tandem queues.     

STATISTICAL VERSUS SALES FORCE–EXECUTIVE OPINION SHORT RANGE FORECASTS: A TIME SERIES ANALYSIS CASE STUDY

This paper presents a comparison of statistical techniques versus a sales force-executive opinion approach to forecasting single item demand over a five year period for a single company. Three statistical techniques are used: Winter's three parameter exponential smoothing model, Brown's harmonic model, and Box-Jenkins methodology. These techniques are compared against the company's forecasts and actual sales for a five year period. The results indicate an interesting area for further research.     

A general space-time model for combinatorial optimization problems (and not only)

We consider the problem of defining a strategy consisting of a set of facilities taking into account also the location where they have to be assigned and the time in which they have to be activated. The facilities are evaluated with respect to a set of criteria. The plan has to be devised respecting some constraints related to different aspects of the problem such as precedence restrictions due to the nature of the facilities. Among the constraints, there are some related to the available budget. We consider also the uncertainty related to the performances of the facilities with respect to considered criteria and plurality of stakeholders participating to the decision. The considered problem can be seen as the combination of some prototypical operations research problems: knapsack problem, location problem and project scheduling. Indeed, the basic brick of our model is a variable x_ilt which takes value 1 if facility i is activated in location l at time t, and 0 otherwise. Due to the conjoint consideration of a location and a time in the decision variables, what we propose can be seen as a general space-time model for operations research problems. We discuss how such a model permits to handle complex problems using several methodologies including multiple attribute value theory and multiobjective optimization. With respect to the latter point, without any loss of the generality, we consider the compromise programming and an interactive methodology based on the Dominance-based Rough Set Approach. We illustrate the application of our model with a simple didactic example.     

Insignificant Betas and the Efficacy of the Sharpe Diagonal Model for Portfolio Selection*

When both practitioners and theorists apply Sharpe's diagonal model [15] to simplify the portfolio selection problem, they assume that the entire covariation structure of each stock (i.e., with all other stocks) is captured in that stock's covariance with the market (or β). Furthermore, it is well known that the selection algorithm itself has a marked tendency to select stocks with the lowest βs, ceteris paribus. When a stock's β is statistically indistinguishable from zero, it is an empirical issue whether the market model is (a) less appropriate for that particular stock relative to those with statistically significant βs; or is (b) a viable model in that the covariance of this stock's rate-of-return with all other stocks' rates-of-return vanishes. The objective of this paper is to distinguish empirically between (a) and (b), and to propose a heuristic which will improve the ex-post performance of the diagonal model. The possible benefits of this heuristic are also demonstrated in a rigorous statistical framework.     

Understanding Construction Supply Chain Management

The purpose of this study was to understand some of the many perplexing questions eluding managers of overwhelmingly complex construction supply chain management (CSCM) based on review of some important research issues pertaining to construction supply chains. This study has illustrated understanding on CSCM using qualitative approach of triangulation applying foundational research methodology and soft system methodology in a coal-based thermal power plant project to validate its complex CSCM systems. The study highlights that a typical CSCM eco-system for the coal-based thermal power plant construction as unit can be considered to operate as a system with sub-systems as concept phase, procurement phase, production phase, installation phase and winding up phase. This study suffers from methodological limitations associated with qualitative research. Finally, this study provides practical insights for research opportunities in the area of interdisciplinary construction projects.     

AXIOMATIC CONJOINT MEASUREMENT

This paper is a tutorial on axiomatic conjoint measurement. It summarizes recent developments, illustrates the use of these developments in investigating ordinal data relations, relates axiomatic and numerical scaling procedures, describes previous applications of conjoint measurement, and suggests future applications in the decision sciences. Emphasis is placed on the contributions axiomatic conjoint measurement could make to the analyses of ordinal data structures.     

Conceptualizing Structurable Tasks in the Development of Knowledge-Based Systems*

Conventional approaches to knowledge-based system (KBS) development are not appropriate for building KBSs when the application task is structurable (i.e., exhibits a certain degree of ill structure). Building a KBS for structurable tasks requires an understanding of the problem-solving strategies used by an expert to manage the ill structure, while at the same time relying on domain theories to understand the structured parts of the task. This paper presents a methodology for developing a knowledge model for structurable tasks during the conceptualization stage of KBS development. This is equivalent to building a logical model for design during the development of conventional information systems. The methodology relies on prior research on the decomposition and characterization of a task based on its various attributes. The paper also illustrates the use of the methodology in the case of KBS development for financial hedging. The paper concludes with some observations about the potential impact of this methodology on other stages in the KBS development process.     

Pioneering Process Research: Andrew Pettigrew's Contribution to Management Scholarship, 1962–2014

This paper assesses Andrew Pettigrew's contribution to management scholarship. This review addresses the process, content, and context of his research career. Chronologically, the process will be subdivided into three distinct phases: the period leading up to the establishment of the Centre for Corporate Strategy and Change, Pettigrew's time with the Centre, and Pettigrew's research since leaving the Centre. The content of Pettigrew's research focussed on big problems and emerging phenomena such as decision‐making, organisational culture, organisation development, strategic change, human resource management, competitiveness, new public management, boards of directors, innovative forms of organising, high‐performing research teams, and business schools. His contextualist methodology for process research will be explicated. Pettigrew's contribution will be contextualised by comparing it with contemporary research. The paper concludes that there is still a need not only to examine big problems and emerging phenomena but also to provide a processual understanding of management reality. There is a need to further develop process research methodologies such as Pettigrew's contextualism, especially with respect to process research methods.     

Design and Assessment Methodology for System Resilience Metrics

By providing objective measures, resilience metrics (RMs) help planners, designers, and decisionmakers to have a grasp of the resilience status of a system. Conceptual frameworks establish a sound basis for RM development. However, a significant challenge that has yet to be addressed is the assessment of the validity of RMs, whether they reflect all abilities of a resilient system, and whether or not they overrate/underrate these abilities. This article covers this gap by introducing a methodology that can show the validity of an RM against its conceptual framework. This methodology combines experimental design methods and statistical analysis techniques that provide an insight into the RM's quality. We also propose a new metric that can be used for general systems. The analysis of the proposed metric using the presented methodology shows that this metric is a better indicator of the system's abilities compared to the existing metrics.     

International Digital Competence

This article evaluates which specific capabilities of a firm enable its internationalization process, with an emphasis on companies with exclusively digital products. The results provide an opportunity to examine the strength of earlier findings in International Entrepreneurship (IE) research, which has largely addressed high-tech ventures that export physical products. We reveal a set of intangible capabilities that are especially relevant to digital companies and their internationalization process, without foreign direct investment (FDI) or with limited outward assets characterized by non-equity entry modes. Using an inductive, multiple-case study methodology, we develop a new construct that we term ‘international digital competence’ (IDC). IDC consists of four capabilities critical to these companies: cross-cultural programming skills, global virtual networks, cross-border digital monetizing adaptability, and international business model reconfiguration. The possession of IDC enables digital companies to expand internationally through an online presence. Therefore, we propose that international online expansion can increase the likelihood of a digital company performing outward assets via non-equity entry modes. We also conceived that the digital company's strategy to achieve non-equity investments is moderated by its entrepreneur's international orientation.     

A 25-year perspective on levels of analysis in leadership research

The purpose of this article is to present a comprehensive 25-year review of the incorporation of levels of analysis into conceptual and empirical leadership research published within Leadership Quarterly throughout its history. We assessed the population of Leadership Quarterly's research (790 research articles) on four key levels of analysis-based issues: (1) explicit statement of the focal level(s) of analysis; (2) appropriate measurement given level of constructs; (3) use of a multi-level data analysis technique; and, (4) alignment of theory and data. Prior reviews regarding levels of analysis incorporation into leadership research have been limited to major research domains. Results revealed that while both conceptual and empirical articles only explicitly state the focal level of analysis in approximately one-third of the articles, appropriate levels-based measurement and alignment between theory and data are relatively strong areas of achievement for the articles within Leadership Quarterly. Multi-level data analysis techniques are used in less than one-fifth of all articles. Although there is room for improvement, there is evidence that Leadership Quarterly is a premier outlet for levels-based leadership research. Given the increasing complexity of organizational science with regard to groups, teams and collectives, Leadership Quarterly has an opportunity to model for organizational research on how to build and test complicated multi-level theories and models.     

Will the Truth Set Us Free? An Exploration of CSR Motive and Commitment

This article examines why firms engage in Corporate Social Responsibility (CSR). Specifically, it investigates the relationship between a firm's motivation to engage in CSR and the depth of its commitment. I propose that the enduring debate over CSR and financial performance is misaligned, and that scholars should instead focus on the underlying components of CSR engagement. This research sheds light on the motivational antecedents of a firm's engagement in CSR and their effect on CSR commitment. Despite calls for scientific investigation of this linkage, it has received scant attention in the literature. Pursuing this area of research requires the construction of measures of CSR motivation and CSR commitment, as prior work generally lacks objective analysis. I present measures and a research methodology that test hypotheses about how CSR motivation relates to different levels of CSR commitment. The results of this research both validate and challenge current theory. This refined understanding of CSR engagement may enhance firm transparency and accountability to stakeholders. It may reduce the uncertainty in both internal and external assessments of firm CSR and the potential for social and financial impact.     

Performance measurement system for business processes

All companies need to know at what level and to what extent they are complying with their objectives. We present a system called PMS-BP (performance measurement system for business processes) which has been designed to define indicators and evaluate company performance. The system is used to measure performance through the integrated management of business processes and to define indicators or parameters at all levels of the company. It also detects the linkages that exist between parameters at different levels and associates them with the company's objectives and strategies through critical success factors. PMS-BP has been built around three components: a methodology, an architecture and a performance measurement structure. The interconnection between the different components makes it easy to constantly trace the relationships between the different performance measurement elements at different levels (enterprise, business entity, processes, etc.). Processes are analysed by means of different types of graphs which make it simple to read and interpret the entire context of the company. We describe a real-life case in which the PMS-BP was applied to an SME in the metal/mechanics sector.