Justifying Social Impact as a Form of Impression Management: Legitimacy Judgements of Social Enterprises’ Impact Accounts

This paper investigates how social enterprises construct accounts to gain legitimacy from the social impact generated by their products and operations. The paper finds that social impact accounts are framed to appeal to two distinct forms of judgement about legitimacy: cognitive and evaluative. Cognitive forms of judgement qualify how well an enterprise shares attributes with an individual's schemas of established actors or roles in society. Evaluative forms of judgement tend to operate more analytically to make comparisons of the relative appropriateness and desirability of multiple enterprises to achieve an audience's goals. The findings show that although legitimizing the social aspects of an enterprise involved justifications aimed at both forms of judgement, legitimizing an enterprise's professionalism relied almost exclusively on evaluative judgements. Moreover, the justifications created to appeal to evaluative judgements relied almost exclusively on financial and operational data, using operational scale as a proxy.     

General Probability‐Time Tradeoff and Intertemporal Risk‐Value Model

This article proposes an intertemporal risk‐value (IRV) model that integrates probability‐time tradeoff, time‐value tradeoff, and risk‐value tradeoff into one unified framework. We obtain a general probability‐time tradeoff, which yields a formal representation form to reflect the psychological distance of a decisionmaker in evaluating a temporal lottery. This intuition of probability‐time tradeoff is supported by robust empirical findings as well as by psychological theory. Through an explicit formalization of probability‐time tradeoff, an IRV model taking into account three fundamental dimensions, namely, value, probability, and time, is established. The object of evaluation in our framework is a complex lottery. We also give some insights into the structure of the IRV model using a wildcatter problem.     

UTILITY MODELS FOR MULTIPLE OBJECTIVE DECISIONS: DO THEY ACCURATELY REPRESENT HUMAN PREFERENCES?*

Recent developments in multiattribute utility (MAU) theory in principle provide a formal basis for applying optimization methods to decision problems involving multiple competing objectives. In practice, MAU models and procedures are rarely applied. This paper is addressed to one major source of reluctance to employ these methods; namely, the belief that they cannot accurately reflect the structure of human preferences. This objection to MAU modeling procedures is inherently empirical. In fact, a large body of psychological research has been addressed to this question. This paper examines the relationship between formal MAU theory and the relevant psychological literature. A critical evaluation of this psychological research suggests that a variety of approaches to MAU modeling will typically yield valid mathematical representations of the preferences of individual decision makers.     

A systematic and critical review of research on respect in leadership

Respect is an important psychological and interpersonal phenomenon that has been included in various theoretical and empirical approaches to leadership for nearly 70 years. In this systematic and critical review article, we provide a comprehensive summary and critique of theories, definitions, measures, and empirical studies of respect in leadership. We first provide an overview of the historical and theoretical background, including the most common theories, definitions, and measures that inform empirical studies of respect in leadership. Second, we present a systematic literature review of empirical studies on respect in leadership, including a critical evaluation of research designs and statistical analyses that support claims of the validity of various conceptualizations of respect that pertain to the study of leadership. Finally, we offer a new working definition of respect in leadership, and an accompanying conceptual framework which informs a number of recommendations for future theory development, empirical research, and leadership practice.     

Forecasting industrial production using structural time series models

Industrial production data series are volatile and often also cyclical. Hence, univariate time series models which allow for these features are expected to generate relatively accurate forecasts of industrial production. A particular class of unobservable components models — structural time series models — is used to generate forecasts of Austrian and German industrial production. A widely applied ARIMA model is used as a baseline for comparison. The empirical results show that the basic structural model generates more accurate forecasts than the ARIMA model when accuracy is measured in terms of size of error or directional change; and that the basic structural model forecasts better than the structural model with a cyclical component included on the basis of numerical measures, and tracking error for month-to-month changes.     

Application of hybrid framework to facilitate lean six sigma implementation: a manufacturing company case experience

This study aims to propose a hybrid framework for lean six sigma (LSS) based on the prioritisation of barriers and solution approaches, to facilitate hassles free applications for handling the challenges of wastes reduction and quality improvement. The study identifies 27 LSS barriers and 22 solution approaches through literature review and utilises feedback from industry experts. For framework, study uses fuzzy AHP-PROMETHEE to prioritise LSS barriers and solution approaches. Incorporating fuzzy sets in analytical hierarchy process (AHP) ensures the optimality of barrier weights. Whereas, preference ranking organisation method for enrichment of evaluations (PROMETHEE) helps in pairwise comparisons of solution approaches with respect to each LSS barrier, facilitating fair judgements. This research guides regarding the development of framework and checks its suitability and robustness through case application of an Indian manufacturing organisation. Authors expect that researchers and practitioners will find study useful for LSS and multi criteria decision making(MCDM) domains.     

Description and Propagation of Uncertainty in Input Parameters in Environmental Fate Models

Today, chemical risk and safety assessments rely heavily on the estimation of environmental fate by models. The key compound‐related properties in such models describe partitioning and reactivity. Uncertainty in determining these properties can be separated into random and systematic (incompleteness) components, requiring different types of representation. Here, we evaluate two approaches that are suitable to treat also systematic errors, fuzzy arithmetic, and probability bounds analysis. When a best estimate (mode) and a range can be computed for an input parameter, then it is possible to characterize the uncertainty with a triangular fuzzy number (possibility distribution) or a corresponding probability box bound by two uniform distributions. We use a five‐compartment Level I fugacity model and reported empirical data from the literature for three well‐known environmental pollutants (benzene, pyrene, and DDT) as illustrative cases for this evaluation. Propagation of uncertainty by discrete probability calculus or interval arithmetic can be done at a low computational cost and gives maximum flexibility in applying different approaches. Our evaluation suggests that the difference between fuzzy arithmetic and probability bounds analysis is small, at least for this specific case. The fuzzy arithmetic approach can, however, be regarded as less conservative than probability bounds analysis if the assumption of independence is removed. Both approaches are sensitive to repeated parameters that may inflate the uncertainty estimate. Uncertainty described by probability boxes was therefore also propagated through the model by Monte Carlo simulation to show how this problem can be avoided.     

Leadership and organizational learning: A multiple levels perspective

We review theoretical and empirical work relevant to the nexus of leadership with organizational learning. We build on the classic distinction between exploration and exploitation and the 4I framework of organizational learning [Crossan, M. M., Lane, H. W., & White, R. E. (1999). An organizational learning framework: From intuition to institution. Academy of Management Review, 24, 522–537.] to present previous research and offer research directions linking leadership constructs and processes of organizational learning at different levels of analysis. For each of these links, we discuss the mediating effect of organizational context and suggest future research directions. This review is integrated using a model and propositions that depict the role of leaders with regard to new and existing learning.     

Reflections on customers’ primary role in markets

Technology developments have transformed the business landscape by accelerating connectivity, transparency and unpredictability. We argue that the most dramatic consequence is not the possibilities created for companies but rather the challenges that emerge as a result of customer behaviour undergoing fundamental changes. Technology transformation has paved the way for empowered customers who are increasingly influencing businesses and markets, and the challenge for practitioners and researchers alike is to make sense of the role of these customers in such business environments. These developments have yielded a need to revise companies’ business models and to innovate new offerings. In scholarly research on marketing, this need has become evident and, beyond suggesting new concepts, completely new marketing management perspectives have been proposed, and each has its own core assumptions and focus. Broader views have emerged, and these stress the applicability of these perspectives not only to the top-level management of businesses but also to any non-commercial organisation.In this article, we reflect on the customer-dominant logic (CDL) of marketing, which stresses the customer's primary role in business. As such, CDL differs from approaches that focus on the provider's perspective. The CDL approach is an expression of the research ideals and goals that are based on the Nordic School of Service Management. We argue that CDL is well aligned with emerging challenges in most markets, both established and nascent, and that it is more managerially relevant than many other perspectives. This is also in line with the recent calls for a soul of relevance for European management research (Chia, 2014; Hernes, 2014).     

Probability Information in Risk Communication: A Review of the Research Literature

Communicating probability information about risks to the public is more difficult than might be expected. Many studies have examined this subject, so that their resulting recommendations are scattered over various publications, diverse research fields, and are about different presentation formats. An integration of empirical findings in one review would be useful therefore to describe the evidence base for communication about probability information and to present the recommendations that can be made so far. We categorized the studies in the following presentation formats: frequencies, percentages, base rates and proportions, absolute and relative risk reduction, cumulative probabilities, verbal probability information, numerical versus verbal probability information, graphs, and risk ladders. We suggest several recommendations for these formats. Based on the results of our review, we show that the effects of presentation format depend not only on the type of format, but also on the context in which the format is used. We therefore argue that the presentation format has the strongest effect when the receiver processes probability information heuristically instead of systematically. We conclude that future research and risk communication practitioners should not only concentrate on the presentation format of the probability information but also on the situation in which this message is presented, as this may predict how people process the information and how this may influence their interpretation of the risk.     

Theoretical Distributions of Optima for Univariate Discrimination of Random Data*

Optimal linear discriminant models maximize percentage accuracy for dichotomous classifications, but are rarely used because a theoretical framework that allows one to make valid statements about the statistical significance of the outcomes of such analyses does not exist. This paper describes an analytic solution for the theoretical distribution of optimal values for univariate optimal linear discriminant analysis, under the assumption that the data are random and continuous. We also present the theoretical distribution for sample sizes up to N= 30. The discovery of a statistical framework for evaluating the performance of optimal discriminant models should greatly increase their use by scientists in all disciplines.     

Local, regional, or global? Geographic orientation and relative financial performance of emerging market multinational enterprises

We extend the regional/global strategies literature by analyzing the relative performance of emerging market (EM) multinational enterprises (MNEs) based on their geographic orientation. We develop a framework showing that firms adopt three geographic orientations—local, regional, and global—and test our framework with the market penetration strategies (sales) of 701 MNEs from 28 EMs during 2000–2006. Our analysis shows that distinguishing among these three geographic segments is important, as not all of these geographic segments enhance firms’ financial performance relative to their industry: a combination of local and global orientations enhances while regional orientation reduces the relative financial performance of EM MNEs.     

Incorporating social networks into leadership development: A conceptual model and evaluation of research and practice

Multilevel and relational views of leadership are expanding the focus of leadership development beyond individuals' knowledge, skills, and abilities to include the networked patterns of social relationships linking members of dyads and larger collectives. In this review, we present a conceptual model explaining how three distinct approaches for network-enhancing leadership development can improve the leadership capacity of individuals and collectives. We then present a review of the leadership development literature and the results of a survey of 282 practitioners to assess the extent to which these approaches have been examined in research and implemented in practice. Our review revealed that leadership research and leadership development practice are outpacing leadership development research in terms of incorporating networks. We aim to spur future research by clarifying the targets, objectives, and underlying mechanisms of each network enhancing leadership development approach in our conceptual model. Further, we identify additional literature, not traditionally considered within the realm of leadership development that may help advance empirical examinations of these approaches.     

Probability and Possibility‐Based Representations of Uncertainty in Fault Tree Analysis

Expert knowledge is an important source of input to risk analysis. In practice, experts might be reluctant to characterize their knowledge and the related (epistemic) uncertainty using precise probabilities. The theory of possibility allows for imprecision in probability assignments. The associated possibilistic representation of epistemic uncertainty can be combined with, and transformed into, a probabilistic representation; in this article, we show this with reference to a simple fault tree analysis. We apply an integrated (hybrid) probabilistic‐possibilistic computational framework for the joint propagation of the epistemic uncertainty on the values of the (limiting relative frequency) probabilities of the basic events of the fault tree, and we use possibility‐probability (probability‐possibility) transformations for propagating the epistemic uncertainty within purely probabilistic and possibilistic settings. The results of the different approaches (hybrid, probabilistic, and possibilistic) are compared with respect to the representation of uncertainty about the top event (limiting relative frequency) probability. Both the rationale underpinning the approaches and the computational efforts they require are critically examined. We conclude that the approaches relevant in a given setting depend on the purpose of the risk analysis, and that further research is required to make the possibilistic approaches operational in a risk analysis context.     

Knowledge Management in Strategic Alliances: A Review of Empirical Evidence

Knowledge‐related and organizational learning processes in alliances have received much attention throughout the last 25 years. The field has generated a rapidly growing body of empirical evidence on how knowledge is managed in alliances. However, the sphere is highly complex, fragmented, incoherent, and heterogeneous in terms of the theoretical approaches applied. This paper presents an integrative and organizing framework for the empirical literature on knowledge management in strategic alliances. It illustrates how the knowledge management outcomes of knowledge creation, transfer and application are determined by four distinct sets of factors: knowledge characteristics, partner characteristics, partner interaction, and active knowledge management. Based on this framework, this review analyses and integrates empirical evidence in order to identify where findings converge and where results conflict. So far, research has focused strongly on singular interrelations between these four sets of factors and the transfer of knowledge. Conversely, the questions of how knowledge is created, retained, retrieved and applied and how the interplay of the different factors affects knowledge management in strategic alliances remain widely unexplored. The review concludes with a summary of the current state of the art in empirical research and discusses some promising avenues for future investigation.     

Use of Advances in Technology for Maritime Risk Assessment

The maritime industry is moving toward a "goal-setting" risk-based regime. This opens the way to safety engineers to explore and exploit flexible and advanced risk modeling and decision-making approaches in the design and operation processes. In this article, following a brief review of the current status of maritime risk assessment, a design/operation selection framework and a design/operation optimization framework are outlined. A general discussion of control engineering techniques and their application to risk modeling and decision making is given. Four novel risk modeling and decision-making approaches are then outlined with illustrative examples to demonstrate their use. Such approaches may be used as alternatives to facilitate risk modeling and decision making in situations where conventional techniques cannot be appropriately applied. Finally, recommendations on further exploitation of advances in general engineering and technology are suggested with respect to risk modeling and decision making.     

Inventory control with seasonality of lead times

The practical challenges posed by the seasonality of lead times have largely been ignored within the inventory control literature. The length of the seasons, as well as the length of the lead times during a season, may demonstrate cyclical patterns over time. This study examines whether inventory control policies that anticipate seasonal lead-time patterns can reduce costs. We design a framework for characterizing different seasonal lead-time inventory problems. Subsequently, we examine the effect of deterministic and stochastic seasonal lead times within periodic review inventory control systems. We conduct a base case analysis of a deterministic system, enabling two established and alternating lead-time lengths that remain valid through known intervals. We identify essential building blocks for developing solutions to seasonal lead-time problems. Lastly, we perform numerical experiments to evaluate the cost benefits of implementing an inventory control policy that incorporates seasonal lead-time lengths. The findings of the study indicate the potential for cost improvements. By incorporating seasonality in length of seasons and length of lead times within the season into the control models, inventory controllers can make more informed decisions when ordering their raw materials. They need smaller buffers against lead-time variations due to the cyclical nature of seasonality. Reductions in costs in our experiments range on average between 18.9 and 26.4% (depending on safety time and the probability of the occurrence of stock out). Therefore, inventory control methods that incorporate seasonality instead of applying large safety stock or safety time buffers can lead to substantial cost reductions.     

A framework for examining leadership in extreme contexts

In this review, we develop a framework to guide future research and to examine the execution of leadership in extreme contexts. We start by defining and distinguishing extreme contexts from crisis and other contexts. A five component typology is developed comprised of magnitude of consequences, form of threat, probability of consequences, location in time and physical or psychological–social proximity. We discuss the unique influences these components have on leadership processes in extreme contexts examining the relevance of organization types such as critical action and high reliability organizations. Further, we present a set of factors that may attenuate or intensify the dimensions comprising an extreme context, thus influencing either a team or organization's ability for adaptive leadership response. Ultimately this framework seeks to develop a richer understanding of extreme contexts to advance the future development of contextualized theories of leadership for extreme contexts.     

Representing employee requirements in labour tour scheduling

In this paper, we use the methodology of simulation to evaluate six approaches for handling employee requirements in an LP-based labour tour scheduling heuristic. We model employee requirements both as minimum acceptable staffing levels—where understaffing is unacceptable—and as target staffing levels—where both under- and overstaffing are acceptable. For each representation of employee requirements, we evaluate forms of the heuristic that use problem-specific and problem-independent information on the costs of employee surpluses and, if appropriate, employee shortages. Over an extensive test data set, the target-staffing approach using problem-specific cost information outperformed all other procedures. Specifically, it generated schedules costing less than 87% of those developed using the approach most commonly found in the literature. Its schedules were also almost 5% cheaper than those of its closest competitor. We discuss the managerial and research implications of the findings and provide suggestions for future research.     

Credibility-based rescheduling model in a double-track railway network: a fuzzy reliable optimization approach

Using a space–time network to represent the choice of train trajectories, this paper proposes a fuzzy optimization framework to reschedule trains in a double-track railway network when the capacity reduction is caused by a low-probability incident. We explicitly introduce a fuzzy variable-based recovery time to capture the uncertainty of incident duration based on professional judgements or empirical estimates. The problem is then formulated as a credibilistic two-stage fuzzy 0–1 integer optimization model to find a reliable operational plan for emergency response guidance. The first stage of the model aims to generate a traversing order on the incident link, and meanwhile the second stage adaptively generates optimized schedules to evaluate the operational plan. Crisp equivalents of mathematical models are further investigated to simplify solution methodologies. The numerical experiments, which are solved by using the GAMS optimization software, demonstrate the effectiveness and efficiency of the proposed approaches.