Purpose A perception gap refers to the differences in perception among the stakeholders regarding any aspect of the supply chain relationship. The purpose of this paper is to investigate the perception gap among service supply chain partners relating to the relative importance of key performance indicators (KPIs) and the association of this gap with service performance. Design/methodology/approach This paper presents an integrative framework that combines statistical methods and data envelopment analysis for computing perception and performance gaps, and for identifying the association between the gaps. The study follows a middle-range theorizing research approach where general inferences are induced from instances, and a theory can be developed from the observation of empirical reality. Findings Analysis of data from a leading global insurance service supply chain suggests that perception gap exists and can be recognised as a factor associated with performance gaps. The results suggest that the perception gap not only affects performance but can also be tracked as a meta-KPI to improve performance throughout the service supply chain. Practical implications The key implication of the presented research is that service companies can identify and resolve the differences in perceptions regarding the importance of the KPIs, by methodologically computing the gaps and tracking them as meta-KPIs. Originality/value The study extends the theoretical boundary of supply chain performance management by introducing the perception and performance gaps as novel meta-KPIs. These meta-KPIs can be computed through the integrative framework developed in the study.
Today's highly competitive business world requires that managers be able to make fast and accurate strategic decisions, as well as learn to adapt to new strategic challenges. This necessity calls for a deep experience and a dynamic understanding of strategic management. The trait of dynamic understanding is mainly the skill of generating additional knowledge and innovative solutions under the new environmental conditions. Building on the concepts of information processing, this paper aims to support managers in constructing new strategic management knowledge, through representing and mining existing knowledge through graph visualization. To this end, a three-stage framework is proposed and described. The framework can enable managers to develop a deeper understanding of the strategic management domain, and expand on existing knowledge through visual analysis. The model further supports a case study that involves unstructured knowledge of profit patterns and the related strategies to succeed using these patterns. The applicability of the framework is shown in the case study, where the unstructured knowledge in a strategic management book is first represented as a semantic network, and then visually mined for revealing new knowledge.
The authors present a benchmarking study on the companies in the Turkish food industry based on their financial data. The aim is to develop a comprehensive benchmarking framework using Data Envelopment Analysis (DEA) and information visualization. Besides DEA, a traditional tool for financial benchmarking based on financial ratios is also incorporated. The consistency/inconsistency between the two methodologies is investigated using information visualization tools. In addition, k-means clustering, a fundamental method from machine learning, is applied. Finally, other relevant data, apart from the financial data, is introduced to the analysis through information visualization to discover new insights into DEA results. The results show that the framework developed is a comprehensive and effective strategy for benchmarking; it can be applied in other industries as well. The study contributes to the literature with a novel methodology that integrates the various benchmarking methods from the fields of operations research, machine learning, and financial analysis.
The authors present a benchmarking study on the companies in the Turkish food industry based on their financial data. The aim is to develop a comprehensive benchmarking framework using Data Envelopment Analysis (DEA) and information visualization. Besides DEA, a traditional tool for financial benchmarking based on financial ratios is also incorporated. The consistency/inconsistency between the two methodologies is investigated using information visualization tools. In addition, k-means clustering, a fundamental method from machine learning, is applied. Finally, other relevant data, apart from the financial data, is introduced to the analysis through information visualization to discover new insights into DEA results. The results show that the framework developed is a comprehensive and effective strategy for benchmarking; it can be applied in other industries as well. The study contributes to the literature with a novel methodology that integrates the various benchmarking methods from the fields of operations research, machine learning, and financial analysis.
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