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To provide an interesting approach for determining interval measures, through the analytic hierarchy process, for integration with social network analysis for knowledge mapping in organizations.

In order to develop improved organizational and business processes through knowledge management, a knowledge audit should be conducted to better understand the knowledge flows in the organization. An important technique to visualize these knowledge flows is the use of a knowledge map. Social network analysis can be applied to develop this knowledge map. Interval measures should be used in the social network analysis in order to determine the strength of the connections between individuals or departments in the organization. This paper applies the analytic hierarchy process to develop these interval measures, and integrates the values within the social network analysis to produce a meaningful knowledge map.

The analytic hierarchy process, when coupled with social network analysis, can be a useful technique for developing interval measures for knowledge‐mapping purposes.

The analytic hierarchy process may become tedious and arduous for use in large social network maps. More research needs to be conducted in this area for scalability.

As social network analysis is gaining more prominence in the knowledge management community, the analytic hierarchy process may be able to provide more valuable measures to determine the strengths of relationships between actors than simply using ordinal numbers.

Coupling the analytic hierarchy process with social network analysis provides a novel approach for future knowledge‐mapping activities.

This study develops a model for selecting Internet advertising networks. The proposed model adopts the “analytic hierarchy process” (AHP) to determine the relative weights of evaluative criteria, then ranks the alternatives and selects the optimum Internet advertising network for advertisers. Additionally, a famous Taiwanese food company is used herein as an example of how an Internet advertising network can be selected using this model. The proposed model helps advertisers to effectively select Internet advertising networks, making it highly applicable for academia and commerce.

The purpose of this paper is to visualize the prioritization among essential factors of cellular manufacturing system (CMS) implementation using the analytic hierarchy process (AHP) and analytic network process (ANP) methods.

Based on literature review, 4 enabler dimensions and 17 CM factors were identified which were validated by experts from academia and industry. Then, AHP and ANP models are proposed in evaluating CMS implementation dimensions and factors. The results are validated using sensitivity analysis.

These models give firms a straightforward and simple to utilize way to deal with CMS efficiently. The two strategies were appeared to be powerful in choosing a strategy for CMS implementation. The two strategies brought about nearly similar outcomes. Both methods consider the particular necessities of the organization through its own accessible ability.

The techniques exhibited in this paper can be utilized by a wide range of organizations for adopting CMS that have a higher impact on performance and thus overall productivity. The two techniques are explained in a step-by-step approach for easier adoption by practitioners.

The strength of the present study is that it is one of the first few to be conducted in perspective for CM implementation factors analysis.

The purpose of this paper is to present a four‐phase dynamic feedback model for supply partner selection in agile supply chains (ASCs). ASCs are commonly used as a response to increasingly dynamic markets. However, partner selection in ASCs is inherently more complex and difficult under conditions of uncertainty and ambiguity as supply chains form and re‐form.

The model draws on both quantitative and qualitative techniques, including the Dempster‐Shafer and optimisation theories, radial basis function artificial neural networks (RBF‐ANN), analytic network process‐mixed integer multi‐objective programming (ANP‐MIMOP), Kraljic's supplier classification matrix and principles of continuous improvement. It incorporates modern computer programming techniques to overcome the information processing difficulties inherent in selecting from amongst large numbers of potential suppliers against multiple criteria in conditions of uncertainty.

The model enables decision makers to make efficient and effective use of the vastly increased amount of data that is available in today's information‐driven society and it offers a comprehensive, systematic and rigorous approach to a complex problem.

The model has two main drawbacks. First, practitioners may find it difficult to match supplier evaluation criteria with the strategic objectives for an ASC. Second, they may perceive the model to be too complex for use when speed is of the essence.

The main contribution of this paper is that, for the first time, it draws together work from previous articles that have described each of the four stages of the model in detail to present a comprehensive overview of the model.

The main intention of this paper is to analyze various factors hindering the growth of the agricultural supply chain and several industry 4.0 technologies to eliminate the same. In addition to a detailed assessment on the implementation of these technologies in agriculture, this manuscript also presents a priority list providing a rank to them based on the relative efficiency of these advancements in addressing these obstacles.

This research proceeds with a two-step process. The particular barriers in the agriculture supply chain and industry 4.0 technologies are determined in the first step. Next, the proposed framework, a combination of data envelopment analysis (DEA) and analytic hierarchy process (AHP), i.e. DEA-AHP, is used to determine a hierarchical structure for the factors and the relative productive efficiencies of the alternatives. The DEA methodology gives a performance analysis of various decision-making units. At the same time, AHP helps in evaluating alternatives weights based on numerous criteria, allowing us to categorize their importance further.

This study reveals how the involvement of technological advancements in agriculture can help manage the supply chain more efficiently. It also justifies how the large quantities of data generated can handle these increasing challenges in the agricultural supply chain.

The results of this study provide a priority list of alternatives based on their final weights. This ranking system can help farmers and the government select the best-suited technology for bringing automation into the agricultural supply chain.

This research is unique as it analyes the general factors hindering the development of the agriculture supply chain while simultaneously providing a list of alternatives based on their relative efficiencies. The study enriches existing literature by providing an analytic approach to determine the weightage of various critical success factors that can help improvise and entrust the real and undeniable requirements of consumers, suppliers and producers.

The purpose of this paper is to propose an integrated qualitative and quantitative approach to the development of a balanced scorecard (BSC) for a real life case company KVIC (Khadi and Village Industries Commission, organic food sector, India).

In this paper the semi‐structured interviews with director, managers, professional consultant, review of published reports and observations made during research work are considered as basis.

This paper illustrates how the use of a mix approach of cause and effect diagram, Interpretive Structural Modeling (ISM) and Analytic Network Process (ANP) can address some of the shortcomings related to the development of BSC in the light of a real life case company KVIC (Khadi Village and Industry Commission, organic food sector India). The paper delivers a complete framework of BSC for the case company.

The paper outlines the limitations of proposed approach in regard to validity of present logical relationships among various objectives of organization in the futuristic environment and indicates the need for a computer software system, which can improve the efficiency of proposed approach.

In the paper a number of case studies report the fact that companies have attempted to derive measures from strategy, based on cause‐and‐effect reasoning, but the claimed link between strategy and measures appeared weak in analysis (Malmi, 2001). The paper establishes the basis for integrating organization's strategic intent with the identification of performance measures and at large development of BSC.

The paper shows that present work demonstrates the use of an innovative approach to the development of performance measurement system at one end while to deliver a workable framework of balanced scorecard for a real life case company is the objective of the other end. The present work encapsulates the philosophy of strategy maps using a mix of quantitative and qualitative approach for a real life case.

The warehouse industry is one of the backbones in the logistics operation which involves several activities i.e. storage, receiving, picking and shipping of goods/cargoes. This study analyzes the most important warehouse productivity indicators for improving warehouse operation efficiency.

This study presents an empirical methodology of the fuzzy analytical hierarchy process (FAHP) method, an integration between the fuzzy logic method with an analytical hierarchy process (AHP) method incorporated with the adoption of quantitative and systems theories under the modern management theory approach.

The results indicate that the weight values of the main criteria which lead by the criterion “Space (0.4005)” at the top ranking, followed by Information System (0.2445), Labor (0.2065) and Equipment (0.1484). In addition, the weight values and ranking of the 16 sub-criteria are also highlighted which the sub-criterion “Warehouse Management System (0.2445)” scores the highest weight value and followed by Storage Space Utilization (0.1043) and Throughput (0.0722) accordingly.

Finally, this research contributed to enrich the literature, while highlighting a series of recommendations on the top three most significant productivity performance indicators that can be useful in further research.

A generic analysis model developed with the adoption of three study theories: quantitative, system and productivity theories.

The rising proportion of internet users in Sub-Saharan Africa and the lack of analytical techniques, as decision support systems, in choosing among alternative internet service providers (ISPs) by consumers underpin this study. The purpose of this paper is to propose an approach for evaluating high-speed internet service offered by ISPs in a sub-Saharan African country.

Using a sample size of 150, pairwise comparisons of two ISPs along five criteria of cost, usability, support, reliability and speed were performed by ten person groups of university students working in various organizations in Ghana and undertaking an online Six Sigma Course. Geometric means were employed to aggregate the scores in 15 groups, and these scores were then normalized and used as input into an analytical hierarchy process grid.

The results show that consumers of internet services highly emphasize the cost attribute of internet provision in their decision making. On the other hand, it was realized that consumers least emphasize the support provided by ISPs in their decision making among alternative ISPs.

This study has sought to provide an analytical framework for assessing the quality of service provided by alternative ISPs in a developing economy’s context. The evaluating criteria in this framework also reveal the key consumer requirements in internet service provision in a developing economy’s environment. This, to a large extent, will inform the marketing strategies of existing ISPs in Ghana as well as prospective ones intending to enter the Ghanaian market. Besides, the National Communication Authority, a regulator of communication services provision in Ghana, will be informed about the performances of the ISPs along five performance criteria. This is expected to aid in their regulatory functions.

Cost overrun is inherent to project chaos, which is one of the key drivers of project failure. The purpose of this paper is to explore the critical elements of complexity-risk interdependency for cost-chaos in the construction management domain by utilizing a multi-criteria decision model.

A total of 12 complexity and 60 risk attributes are initially identified from the literature and using expert’s judgements. For the development of a structured hierarchy of key complexity and risk drivers, a real-time Delphi process is adopted for recording and evaluating the responses from experts. Afterwards, a pair-wise comparison using analytical network processing is performed to measure complexity-risk interdependencies against cost alternatives.

The findings of the integrated priority decision index (IPDI) suggest that uncertainties related to contingency and escalation costs are the main sources of cost overrun in project drift, along with the key elements such as “the use of innovative technology,” “multiple contracts,” “low advance payment,” “change in design,” “unclear specifications” and “the lack of experience” appear to be more significant to chaos in complexity-risk interdependency network.

This study did not address the uncertainty and vulnerability exit in the judgment process, therefore, this framework can be extended using fuzzy logic to better evaluate the significance of cost-chaos drivers.

These results may assist the management of cost overrun to avoid chaos in a project. The proposed model can be applied within project risk management practices to make better-informed technical decisions in the early phases of the project life cycle where uncertainty is high.

This research addresses the importance of cost overruns as a source of project chaos in dynamic systems where projects reach the edge of chaos and progress stops. A new IPDI index contributes toward evaluating the severity of complexity and risk and their interdependencies which create cost-chaos in infrastructure transport projects.

In this study, the deployment of an integrated Quality Function Deployment (QFD) decision framework is presented to help cities design targeted strategies to become “smart”. Arguably smart cities leverage advanced technologies to enhance their smartness to improve everyday urban life. To this end, a QFD – Analytic Hierarchy Process – Analytic Network Process (QFD-AHP-ANP) framework is proposed to deliver guidance for selecting the appropriate mix of smart technologies based on the specific smart needs of each city.

The AHP and ANP methods are incorporated into QFD to enhance its methodological robustness in formulating the decision problem. AHP accurately captures and translates the “Voice of the Experts” into prioritized “Smart City” dimensions, while establishing inter-relationships between these dimensions and “Smart City Technologies”. Meanwhile, ANP explores tradeoffs among the technologies, enabling well-informed decisions. The framework’s effectiveness is evaluated through an illustrative application in the city of Thessaloniki.

Applying the framework to this real-world context confirms its practicality and utility, demonstrating its ability to particularize local, social, political, environmental and economic trends through the resulting mix of technologies in smart urban development strategies.

The importance of this study lies in several aspects. Firstly, it introduces a novel QFD decision framework tailored for smart city strategic planning. Secondly, it contributes to the operationalization of the smart city concept by providing guidance for cities to effectively adopt smart technologies. Finally, this study represents a new field of application for QFD, expanding its scope beyond its traditional domains.