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All Journal International Journal of Financial, Accounting, and Management Annals of Management and Organization Research
Bazyar, Afshar
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Journal : Annals of Management and Organization Research

 1 
The dynamics of digital banking adoption: Insights from Iran's context on marketing strategies and personal competence Bazyar, Afshar
Annals of Management and Organization Research Vol. 5 No. 4 (2024): May
Publisher : goodwood publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/amor.v5i4.2003

Abstract

Purpose: This study investigates the factors affecting customers' digital banking adoption using the Technology Adoption Model (TAM), supplemented by Bank Marketing Activities (BMA) and Bank Personal Competence (BPC). Research Methodology: A total of 271 participants were analyzed quantitatively using Structural Equation Modeling (SEM) with IBM AMOS 26. Results: The findings indicate that both Bank Marketing Activities (BMA) and Bank Personal Competence (BPC) play significant roles in augmenting the perceived ease of use and usefulness of digital banking. Specifically, BPC demonstrates greater efficacy in enhancing ease of use, whereas BMA affects both ease of use and usefulness. Key components of effective bank marketing include personnel expertise, proactive service delivery, and the effective handling of customer dissatisfaction. Moreover, essential competencies for bank staff include being responsive, adept at problem solving, and adhering to ethical standards. Limitations: The applicability of this study's findings is primarily focused on the educational environment. Contribution: This study expands the theory of technology adoption, particularly within the realm of marketing functions. It offers valuable managerial insights into the prioritization of bank services and the development of personnel competencies aimed at bolstering the adoption of digital banking services.
Multi-objective planning for a multi-echelon supply chain using parameter-tuned meta-heuristics Bazyar, Afshar; Abbasi, Morteza
Annals of Management and Organization Research Vol. 7 No. 1 (2025): August
Publisher : goodwood publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/amor.v7i1.2542

Abstract

Purpose: This study presents a tri-objective model for the integrated planning of production and distribution within a multi-level supply chain network that encompasses multiple product types and time periods. Research methodology: The supply chain network includes manufacturer plants (MPs), distribution centers (DCs), retailers, and final customers. The proposed model aims to minimize total supply chain costs, ensure timely delivery of products to customers, and reduce the lost demand rate. Classified as a linear integer programming problem, which is NP-Hard, the model’s complexity is addressed using two multi-objective meta-heuristic approaches based on the Pareto method: the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and the Non-Dominated Ranking Genetic Algorithm (NRGA). The Taguchi method is employed to optimize the input parameters of these algorithms. Results: The performance of the proposed solution methods is evaluated through various test problems of different dimensions. Statistical analyses confirm the effectiveness and reliability of both algorithms in achieving the defined objectives. Conclusions: The findings highlight that multi-objective meta-heuristic approaches, when parameter-tuned appropriately, provide efficient and practical solutions for integrated supply chain planning, offering a balance among cost, service level, and demand fulfillment. Limitations: The study acknowledges the inherent complexity of the problem and the dependency of meta-heuristic outputs on parameter settings, which may influence solution robustness. Contribution: This research contributes to the literature by providing a robust framework for optimizing production and distribution in complex supply chain networks, delivering insights into the application of advanced algorithmic strategies in operational planning.
Multi-objective planning for a multi-echelon supply chain using parameter-tuned meta-heuristics Bazyar, Afshar; Abbasi, Morteza
Annals of Management and Organization Research Vol. 7 No. 1 (2025): August
Publisher : goodwood publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/amor.v7i1.2542

Abstract

Purpose: This study presents a tri-objective model for the integrated planning of production and distribution within a multi-level supply chain network that encompasses multiple product types and time periods. Research methodology: The supply chain network includes manufacturer plants (MPs), distribution centers (DCs), retailers, and final customers. The proposed model aims to minimize total supply chain costs, ensure timely delivery of products to customers, and reduce the lost demand rate. Classified as a linear integer programming problem, which is NP-Hard, the model’s complexity is addressed using two multi-objective meta-heuristic approaches based on the Pareto method: the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and the Non-Dominated Ranking Genetic Algorithm (NRGA). The Taguchi method is employed to optimize the input parameters of these algorithms. Results: The performance of the proposed solution methods is evaluated through various test problems of different dimensions. Statistical analyses confirm the effectiveness and reliability of both algorithms in achieving the defined objectives. Conclusions: The findings highlight that multi-objective meta-heuristic approaches, when parameter-tuned appropriately, provide efficient and practical solutions for integrated supply chain planning, offering a balance among cost, service level, and demand fulfillment. Limitations: The study acknowledges the inherent complexity of the problem and the dependency of meta-heuristic outputs on parameter settings, which may influence solution robustness. Contribution: This research contributes to the literature by providing a robust framework for optimizing production and distribution in complex supply chain networks, delivering insights into the application of advanced algorithmic strategies in operational planning.
The dynamics of digital banking adoption: Insights from Iran's context on marketing strategies and personal competence Bazyar, Afshar
Annals of Management and Organization Research Vol. 5 No. 4 (2024): May
Publisher : goodwood publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/amor.v5i4.2003

Abstract

Purpose: This study investigates the factors affecting customers' digital banking adoption using the Technology Adoption Model (TAM), supplemented by Bank Marketing Activities (BMA) and Bank Personal Competence (BPC). Research Methodology: A total of 271 participants were analyzed quantitatively using Structural Equation Modeling (SEM) with IBM AMOS 26. Results: The findings indicate that both Bank Marketing Activities (BMA) and Bank Personal Competence (BPC) play significant roles in augmenting the perceived ease of use and usefulness of digital banking. Specifically, BPC demonstrates greater efficacy in enhancing ease of use, whereas BMA affects both ease of use and usefulness. Key components of effective bank marketing include personnel expertise, proactive service delivery, and the effective handling of customer dissatisfaction. Moreover, essential competencies for bank staff include being responsive, adept at problem solving, and adhering to ethical standards. Limitations: The applicability of this study's findings is primarily focused on the educational environment. Contribution: This study expands the theory of technology adoption, particularly within the realm of marketing functions. It offers valuable managerial insights into the prioritization of bank services and the development of personnel competencies aimed at bolstering the adoption of digital banking services.
The dynamics of digital banking adoption: Insights from Iran's context on marketing strategies and personal competence Bazyar, Afshar
Annals of Management and Organization Research Vol. 5 No. 4 (2024): May
Publisher : goodwood publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/amor.v5i4.2003

Abstract

Purpose: This study investigates the factors affecting customers' digital banking adoption using the Technology Adoption Model (TAM), supplemented by Bank Marketing Activities (BMA) and Bank Personal Competence (BPC). Research Methodology: A total of 271 participants were analyzed quantitatively using Structural Equation Modeling (SEM) with IBM AMOS 26. Results: The findings indicate that both Bank Marketing Activities (BMA) and Bank Personal Competence (BPC) play significant roles in augmenting the perceived ease of use and usefulness of digital banking. Specifically, BPC demonstrates greater efficacy in enhancing ease of use, whereas BMA affects both ease of use and usefulness. Key components of effective bank marketing include personnel expertise, proactive service delivery, and the effective handling of customer dissatisfaction. Moreover, essential competencies for bank staff include being responsive, adept at problem solving, and adhering to ethical standards. Limitations: The applicability of this study's findings is primarily focused on the educational environment. Contribution: This study expands the theory of technology adoption, particularly within the realm of marketing functions. It offers valuable managerial insights into the prioritization of bank services and the development of personnel competencies aimed at bolstering the adoption of digital banking services.
Multi-objective planning for a multi-echelon supply chain using parameter-tuned meta-heuristics Bazyar, Afshar; Abbasi, Morteza
Annals of Management and Organization Research Vol. 7 No. 1 (2025): August
Publisher : goodwood publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/amor.v7i1.2542

Abstract

Purpose: This study presents a tri-objective model for the integrated planning of production and distribution within a multi-level supply chain network that encompasses multiple product types and time periods. Research methodology: The supply chain network includes manufacturer plants (MPs), distribution centers (DCs), retailers, and final customers. The proposed model aims to minimize total supply chain costs, ensure timely delivery of products to customers, and reduce the lost demand rate. Classified as a linear integer programming problem, which is NP-Hard, the model’s complexity is addressed using two multi-objective meta-heuristic approaches based on the Pareto method: the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and the Non-Dominated Ranking Genetic Algorithm (NRGA). The Taguchi method is employed to optimize the input parameters of these algorithms. Results: The performance of the proposed solution methods is evaluated through various test problems of different dimensions. Statistical analyses confirm the effectiveness and reliability of both algorithms in achieving the defined objectives. Conclusions: The findings highlight that multi-objective meta-heuristic approaches, when parameter-tuned appropriately, provide efficient and practical solutions for integrated supply chain planning, offering a balance among cost, service level, and demand fulfillment. Limitations: The study acknowledges the inherent complexity of the problem and the dependency of meta-heuristic outputs on parameter settings, which may influence solution robustness. Contribution: This research contributes to the literature by providing a robust framework for optimizing production and distribution in complex supply chain networks, delivering insights into the application of advanced algorithmic strategies in operational planning.
Co-Authors Abbasi, Morteza