Search results

This paper examines the theoretical issues and research themes of business and management impact. Our empirical setting is the UK Research Excellence Framework 2014 (REF 2014) and the focus is on the nature of research impact. Stakeholders, including Governments, now expect academic outputs to translate to real world benefits beyond the narrow bibliometric type metrics.

Despite decades of academic literature devoted to business and management research impact, current theories cannot explain the apparent disconnect between academic, economic and societal practice. Adopting a UK Business and Management perspective to frame our investigation, we consider the highly contested rhetorical question – What are the current themes and impacts of Business and Management research?

We propose a definition for research impact and consider its measurement. Then, using the 410 Impact Case Studies submitted to REF 2014 #x2013; Unit of Assessment 19, business and management, we examine how high impact unfolds. The implications for business and management research impact from the perspectives of economic, knowledge and responsibility impacts are considered.

The purpose of this paper is to propose an innovative integration method based on decision-theoretic rough set and the extended VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) methods to address the resilient-sustainable supplier selection and order allocation (SS/OA) problem.

Specifically, a two-stage approach is designed in this paper. First, the decision-theoretic rough set is employed to calculate the rough number for coping with the subjective uncertainty of data and assigning the weights for a resilient-sustainable evaluation criterion. On this basis, the supplier resilient-sustainable performance is ranked in combination with the extended VIKOR method. Second, a novel multi-objective optimization model is proposed that applies an improved genetic algorithm to select the resilient-sustainable supplier and allocate the corresponding order quantity under a multi-tier supplier network.

The results reveal that joint consideration of resilience and sustainability is essential in the SS/OA process. The method proposed in this study based on decision-theoretic rough sets and the extended VIKOR method can handle imprecise information flexibly, reduce information loss and obtain acceptable solutions for decision-makers. Numerical cases validate that this integrated approach can combine resilience and sustainability for effective and efficient SS/OA.

This paper provides industry managers with a new perspective on SS/OA from a resilience and sustainability perspective as a basis for best practices for industry resilience and sustainability. The proposed method helps to evaluate the resilient-sustainable performance of potential suppliers, which is applicable to solving real-world SS/OA problems and has important practical implications for the resilient-sustainable development of supply chains.

The two interrelated priorities of resilience and sustainability have emerged as key strategic challenges in SS/OA issues. This paper is the first study of this issue that uses the proposed integrated approach.

Purchase conversion prediction aims to improve user experience and convert visitors into real buyers to drive sales of firms; however, the total conversion rate is low, especially for e-retailers. To date, little is known about how e-retailers can scientifically detect users' intents within a purchase conversion funnel during their ongoing sessions and strategically optimize real-time marketing tactics corresponding to dynamic intent states. This study mainly aims to detect a real-time state of the conversion funnel based on graph theory, which refers to a five-class classification problem in the overt real-time choice decisions (RTCDs)—click, tag-to-wishlist, add-to-cart, remove-from-cart and purchase—during an ongoing session.

The authors propose a novel graph-theoretic framework to detect different states of the conversion funnel by identifying a user's unobserved mindset revealed from their navigation process graph, namely clickstream graph. First, the raw clickstream data are identified into individual sessions based on a 30-min time-out heuristic approach. Then, the authors convert each session into a sequence of temporal item-level clickstream graphs and conduct a temporal graph feature engineering according to the basic, single-, dyadic- and triadic-node and global characteristics. Furthermore, the synthetic minority oversampling technique is adopted to address with the problem of classifying imbalanced data. Finally, the authors train and test the proposed approach with several popular artificial intelligence algorithms.

The graph-theoretic approach validates that users' latent intent states within the conversion funnel can be interpreted as time-varying natures of their online graph footprints. In particular, the experimental results indicate that the graph-theoretic feature-oriented models achieve a substantial improvement of over 27% in line with the macro-average and micro-average area under the precision-recall curve, as compared to the conventional ones. In addition, the top five informative graph features for RTCDs are found to be Transitivity, Edge, Node, Degree and Reciprocity. In view of interpretability, the basic, single-, dyadic- and triadic-node and global characteristics of clickstream graphs have their specific advantages.

The findings suggest that the temporal graph-theoretic approach can form an efficient and powerful AI-based real-time intent detecting decision-support system. Different levels of graph features have their specific interpretability on RTCDs from the perspectives of consumer behavior and psychology, which provides a theoretical basis for the design of computer information systems and the optimization of the ongoing session intervention or recommendation in e-commerce.

To the best of the authors' knowledge, this is the first study to apply clickstream graphs and real-time decision choices in conversion prediction and detection. Most studies have only meditated on a binary classification problem, while this study applies a graph-theoretic approach in a five-class classification problem. In addition, this study constructs temporal item-level graphs to represent the original structure of clickstream session data based on graph theory. The time-varying characteristics of the proposed approach enhance the performance of purchase conversion detection during an ongoing session.

Most micro‐oriented research has assumed that leaders operate within a formally designed, pre‐specified and stable structural hierarchy. In the emerging knowledge economy, however, the watchwords have become flexibility and speed. Today's business practices feature temporary task forces, project teams, joint ventures, and alliances. The authors aim to draw from the political science literature, presenting methodology for examining team leadership in context that augments and enhances the analyses that can be obtained with commonly used correlational/regression models.

More specifically, the authors will develop a detailed analysis of the potential applicability of the set‐theoretic approach for elucidating a configural analysis of team leadership, demonstrating that this method provides insight into the data that complements that provided by regression analysis.

The authors examine the findings from more traditional approaches to analysis, and set‐theoretic analysis, and demonstrate that set‐theoretic analysis provides additional insights into the phenomena in question.

The possibilities for future research in the organizational leadership literature using set‐theoretic analysis are endless. The method is appropriate for theory development, theory confirmation, exploration, and more. Our current theoretical models are lacking in complexity, partly due to the limitations of existing methodology, and our advice to organizations would be significantly enriched were we able to establish that there are alternate means to the desired outcome, based on the context. Set‐theoretic methodology promises to enrich and enhance our field, particularly if we use it iteratively with the currently common methodologies, to develop richer and more contextually based theories.

Set‐theoretic methods may well provide an additional set of tools for better understanding leadership in the context in which it occurs.

The purpose of this paper is to propose the use of graph theoretic structural modeling for assessing the possible reduction in complexity of the work flow procedures in an organization due to lean initiatives. A tool to assess the impact of lean initiative on complexity of the system at an early stage of decision making is proposed.

First, the permanent function-based graph theoretic structural model has been applied to understand the complex structure of a manufacturing system under consideration. The model helps by systematically breaking it into different sub-graphs that identify all the cycles of interactions among the subsystems in the organization in a systematic manner. The physical interpretation of the existing quantitative methods linked to graph theoretic methodology, namely two types of coefficients of dissimilarity, has been used to evolve the new measures of organizational complexity. The new methods have been deployed for studying the impact of different lean initiatives on complexity reduction in a case industrial organization.

The usefulness and the application of new proposed measures of complexity have been demonstrated with the help of three cases of lean initiatives in an industrial organization. The new measures of complexity have been proposed as a credible tool for studying the lean initiatives and their implications.

The paper may lead many researchers to use the proposed tool to model different cases of lean manufacturing and pave a new direction for future research in lean manufacturing.

The paper demonstrates the application of new tools through cases and the tool may be used by practitioners of lean philosophy or total quality management to model and investigate their decisions.

The proposed measures of complexity are absolutely new addition to the tool box of graph theoretic structural modeling and have a potential to be adopted by practical decision makers to steer their organizations though such decisions before the costly interruptions in manufacturing systems are tried on ground.

This chapter explores how a configurational approach and set-theoretic methods can contribute to a deeper and more nuanced understanding of organizational networks and network relations. This is especially true for the study of “whole networks” of organizations where data collection difficulties often limit the sample size (Provan, Fish, & Sydow, 2007). We present two empirical examples of current research on whole networks, demonstrating how qualitative comparative analysis (QCA) can be used to analyze organizational networks. We then discuss the methodological and theoretical implications of the configurational approach for future organizational network research.

A discipline is bound by some combination of a shared subject matter, shared theory, and shared technique. Yet modern economics is seemingly without limit to its domain. As a discipline without a shared subject matter, what is the binding force of economics today? The authors combine topic modeling and text analysis to analyze different approaches to inquiry within the discipline of economics. The authors find that the importance of theory has declined as economics has increasingly become defined by its empirical techniques. The authors question whether this trajectory is stable in the long run as the binding force of the discipline.

The study here responds to the view that the crucial problem in strategic management (research) is firm heterogeneity – why firms adopt different strategies and structures, why heterogeneity persists, and why competitors perform differently. The present study applies complexity theory tenets and a “neo-configurational perspective” of Misangyi et al. (2016) in proposing complex antecedent conditions affecting complex outcome conditions. Rather than examining variable directional relationships using null hypotheses statistical tests, the study examines case-based conditions using somewhat precise outcome tests (SPOT). The complex outcome conditions include firms with high financial performances in declining markets and firms with low financial performances in growing markets – the study focuses on seemingly paradoxical outcomes. The study here examines firm strategies and outcomes for separate samples of cross-sectional data of manufacturing firms with headquarters in one of two nations: Finland (n = 820) and Hungary (n = 300). The study includes examining the predictive validities of the models. The study contributes conceptual advances of complex firm orientation configurations and complex firm performance capabilities configurations as mediating conditions between firmographics, firm resources, and the two final complex outcome conditions (high performance in declining markets and low performance in growing markets). The study contributes by showing how fuzzy-logic computing with words (Zadeh, 1966) advances strategic management research toward achieving requisite variety to overcome the theory-analytic mismatch pervasive currently in the discipline (Fiss, 2007, 2011) – thus, this study is a useful step toward solving the crucial problem of how to explain firm heterogeneity.

To determine the role of status information conveyance in a negative reward allocation setting.

Using previously published experimental data, we test the relative effects of status information conveyed by expressive and indicative status cues on the allocation of a negative reward. Further, we construct an alternative graph theoretic model of expectation advantage which is also tested to determine its model fit relative to the classic model of Reward Expectations Theory.

Results provide strong support for the conclusion that status information conveyed by expressive status cues influences reward allocations more than information conveyed by indicative cues. We also find evidence that our alternative graph theoretic model of expectation advantage improves model fit.

This research is the first to test the relative impact of expressive versus indicative status cues on the allocation of negative rewards and shows that status characteristics can have differential impacts on these allocations contingent on how characteristics are conveyed. Furthermore, the research suggests a graph theoretic model that allows for this differentiation based on information conveyance and provides empirical support for its structure in a negative reward allocation environment.

Future research is required to validate the results in positive reward situations.

The results show that an individual’s expectations are altered by varying the manner in which status information is presented, thereby influencing the construction and maintenance of status hierarchies and the inequalities those structures generate. Thus, this research has implications for any group or evaluative task where status processes are relevant.

The contrast of multilevel and comparative research may seem counterintuitive at first. After all, one might argue that comparative research on organizations by necessity spans several levels of analysis (Rokkan, 1966). Yet, multilevel and comparative research on organizations present rather distinct traditions in organization studies, each with its own epistemological assumptions and associated methods. Accordingly, an approach that aims to incorporate both multilevel and comparative ideas needs to start with taking inventory of these prior literatures to situate itself. In the following, we thus turn to the literatures on multilevel and comparative research as different traditions with surprisingly little overlap.