Leicester Castle Business School

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Browsing Leicester Castle Business School by Author "Abbasi Kamardi, Ali Asghar"

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    Differential game approach to pricing and advertising decisions
    (Elsevier, 2021-07-13) Amoozad Mahdiraji, Hannan; Hatami-Marbini, A.; Mohammadi Moazed, Niloofar; Ansari, Manuchehr; Abbasi Kamardi, Ali Asghar
    This study proposes a model to make concurrent decisions on dynamic pricing and advertising to maximise firms' profitability over an infinite time horizon in a duopoly market. To this end, the Nerlove-Arrow pricing and advertising model is designed in the presence of shifting costs in a dynamic duopolistic competition as a differential game. The Nash equilibrium solution is defined based upon a set of Hamilton–Jacobi–Bellman. Four scenarios are applied for economic interpretations and the efficacy of the model.
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    Evaluating key capabilities for developing global collaborative networks using a multi-layer decision-making approach
    (Emerald, 2021-07-07) Amoozad Mahdiraji, Hannan; Hafeez, Khalid; Abbasi Kamardi, Ali Asghar; Garza-Reyes, Jose Arturo
    Purpose – This paper proposes a multi-layer hybrid decision-making approach to evaluate the capability alternatives for developing a collaborative network to operate in the international market. Design/methodology/approach – The present study is contextualised in the Iranian pistachio export industry. An extensive review of the state-of-the-art literature on supplier collaboration was conducted to identify key capabilities that are essential to establish a collaborative network. The set of defined capabilities were then optimised through interviews with 14 experts from the relevant industry, academics and export authorities. A combination of the fuzzy Delphi method and the best–worst method (BWM) approach was, respectively, used to reduce the number of capability alternatives and assign priority weights to these alternatives. Subsequently, a weighted aggregated sum product assessment method (WASPAS) was employed to rank and evaluate the ability to creating a collaborative network for the export of pistachio. Findings – From the extant literature review, 18 capabilities for the formation of coordination networks in the internationalmarketswere identified.Then, the prominent indicators in forming a global networkwere extracted. After ranking the top pistachio export countries/regions to formalise an efficient collaborative network, it was revealed that although Iran exports approximately 30% of the global market, it falls behind the USA and European Union. The competitors have scored higher in critical criteria, including “trust and commitment”, “strategy and management”, “managerial control and standardization” and “financial resources”. Originality/value – The proposed hybrid approach encompassing fuzzy Delphi–BWM–WASPAS offers to solve the capability evaluation and selection as well as ranking the possible alternative to formalise a collaborative network in an integrated fashion. This combination of methods is capable to first identify the most important factors, thenmeasuring their importance and eventually rank the possible alternatives. The suggested framework provides an approach to deal with the uncertainty of global collaborative network formation.
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    A multi-attribute data mining model for rule extraction and service operations benchmarking
    (Emerald, 2021-06-01) Amoozad Mahdiraji, Hannan; Tavana, Madjid; Mahdiyani, Pouya; Abbasi Kamardi, Ali Asghar
    Purpose Customer differences and similarities play a crucial role in service operations, and service industries need to develop various strategies for different customer types. This study aims to understand the behavioral pattern of customers in the banking industry by proposing a hybrid data mining approach with rule extraction and service operation benchmarking. Design/methodology/approach The authors analyze customer data to identify the best customers using a modified recency, frequency and monetary (RFM) model and K-means clustering. The number of clusters is determined with a two-step K-means quality analysis based on the Silhouette, Davies–Bouldin and Calinski–Harabasz indices and the evaluation based on distance from average solution (EDAS). The best–worst method (BWM) and the total area based on orthogonal vectors (TAOV) are used next to sort the clusters. Finally, the associative rules and the Apriori algorithm are used to derive the customers' behavior patterns. Findings As a result of implementing the proposed approach in the financial service industry, customers were segmented and ranked into six clusters by analyzing 20,000 records. Furthermore, frequent customer financial behavior patterns were recognized based on demographic characteristics and financial transactions of customers. Thus, customer types were classified as highly loyal, loyal, high-interacting, low-interacting and missing customers. Eventually, appropriate strategies for interacting with each customer type were proposed. Originality/value The authors propose a novel hybrid multi-attribute data mining approach for rule extraction and the service operations benchmarking approach by combining data mining tools with a multilayer decision-making approach. The proposed hybrid approach has been implemented in a large-scale problem in the financial services industry.