Mohammad Hossein Shahidzadeh

@sbu.ac.ir

Management and Accounting Faculty, Department of Industrial and Information Management
Shahid Beheshti University

Mohammad Hossein Shahidzadeh


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https://researchid.co/shahidzadeh

RESEARCH, TEACHING, or OTHER INTERESTS

Industrial relations, Industrial and Manufacturing Engineering, Information Systems and Management, Management Science and Operations Research
9

Scopus Publications

235

Scholar Citations

7

Scholar h-index

6

Scholar i10-index

Scopus Publications

  • Revolutionizing reverse supply chain decision-making: Deep social media analysis in qualitative comparative analysis
    Mohammad Hossein Shahidzadeh, Sajjad Shokouhyar
    Computers and Industrial Engineering, 2025
  • Revealing the sustainable consumption barriers based on the product-service system: social media analytics approach
    Ali Pourranjbar, Sajjad Shokouhyar, Mohammad Hossein Shahidzadeh, Ethan Nikookar, Sina Shokoohyar, et al.
    Industrial Management and Data Systems, 2024
    PurposeGiven the growing emphasis on environmental consciousness and sustainability as core principles within most companies, product-service systems are recognized as strategic approaches to achieving sustainability objectives. Consequently, understanding consumer acceptance of these systems is of paramount importance. This study seeks to explore users' perspectives on the barriers that impede the adoption of product-service systems, intending to prioritize these obstacles.Design/methodology/approachThis study utilizes a social media-based approach, specifically analyzing tweets related to Zipcar, an American car rental company that exemplifies a usage-oriented product-service system. The analysis identifies the factors influencing the acceptance of this system. The study utilizes topic modeling and sentiment analysis techniques to analyze the tweets. The opportunity value of each topic is determined, aiding in the identification of topics that require improvement. Furthermore, the interrelation between topics is explored, followed by correlation analysis to assess their significance.FindingsEight topics strongly related to the keywords are identified. Among them, “responsiveness”, “responsibility”, and “trust” hold the highest opportunity values. The findings emphasize the importance of service providers proactively addressing the obstacles that impede consumers' willingness to adopt product-service systems. Prioritization should be given to topics with higher opportunity values.Originality/valueThis research uncovers the primary obstacles to adopting the product-service system by directly considering consumer opinions and providing a prioritized list of these obstacles.
  • Unveiling just-in-time decision support system using social media analytics: a case study on reverse logistics resource recycling
    Mohammad Hossein Shahidzadeh, Sajjad Shokouhyar
    Industrial Management and Data Systems, 2024
    PurposeIn recent times, the field of corporate intelligence has gained substantial prominence, employing advanced data analysis techniques to yield pivotal insights for instantaneous strategic and tactical decision-making. Expanding beyond rudimentary post observation and analysis, social media analytics unfolds a comprehensive exploration of diverse data streams encompassing social media platforms and blogs, thereby facilitating an all-encompassing understanding of the dynamic social customer landscape. During an extensive evaluation of social media presence, various indicators such as popularity, impressions, user engagement, content flow, and brand references undergo meticulous scrutiny. Invaluable intelligence lies within user-generated data stemming from social media platforms, encompassing valuable customer perspectives, feedback, and recommendations that have the potential to revolutionize numerous operational facets, including supply chain management. Despite its intrinsic worth, the actual business value of social media data is frequently overshadowed due to the pervasive abundance of content saturating the digital realm. In response to this concern, the present study introduces a cutting-edge system known as the Enterprise Just-in-time Decision Support System (EJDSS).Design/methodology/approachLeveraging deep learning techniques and advanced analytics of social media data, the EJDSS aims to propel business operations forward. Specifically tailored to the domain of marketing, the framework delineates a practical methodology for extracting invaluable insights from the vast expanse of social data. This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.FindingsTo substantiate the efficacy of the EJDSS, a detailed case study centered around reverse logistics resource recycling is presented, accompanied by experimental findings that underscore the system’s exceptional performance. The study showcases remarkable precision, robustness, F1 score, and variance statistics, attaining impressive figures of 83.62%, 78.44%, 83.67%, and 3.79%, respectively.Originality/valueThis scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.
  • Mastering supply chain’s decision-making establishing SDG’s goal: a social media analytics study of the electronic devices in developing and developed countries
    Sajjad Shokouhyar, Mohammad Hossein Shahidzadeh
    Annals of Operations Research, 2024
  • Toward the closed-loop sustainability development model: a reverse logistics multi-criteria decision-making analysis
    Mohammad Hossein Shahidzadeh, Sajjad Shokouhyar
    Environment Development and Sustainability, 2023
  • Discovering the secret behind managing WEEE: deep learning method in industry 4.0
    Mohammad Hossein Shahidzadeh, Sajjad Shokouhyar, Aida Safari, Erfan Babaee Tirkolaee, Sina Shokoohyar
    Annals of Operations Research, 2023
  • Unscramble social media power for waste management: A multilayer deep learning approach
    Mohammad Hossein Shahidzadeh, Sajjad Shokouhyar, Fatemeh Javadi, Sina Shokoohyar
    Journal of Cleaner Production, 2022
  • Shedding light on the reverse logistics’ decision-making: a social-media analytics study of the electronics industry in developing vs developed countries
    Mohammad Hossein Shahidzadeh, Sajjad Shokouhyar
    International Journal of Sustainable Engineering, 2022
    Growing population leads to generating more waste and depletion of natural resources. Moreover, the cost of supplying some resources has increased substantially. Hence, the manufacturer is trying to focus on planning to get back old or partially/wholly unusable products and make the best disposition decisions on them. This research aims to build a multi-industry applied model using the deep learning method in social media analysis to make the best decision for returning products in reverse logistics, along with the sustainability and circular economy concerns. Furthermore, we outline the usage of social network analytics in aligning consumers’ expectations with supply chain policies, strategies, and decisions. An industry benchmark concerning circular economy concepts can be attained by applying the proposed model to different industries. We have proposed a generalisable model using social media analytics, consumer sentiment analysis, reverse logistics, and circular economy theory to attain a circular supply chain regarding sustainability concerns. Applying the proposed model to the electronics industry as a case study, the model was further validated with Twitter data analysis of developing versus developed countries for laptop devices. We collected over 70-million tweets using the Twitter Application Programming Interface (API) over fifteen months. The results approved the proposed model by leveraging the Twitter geolocation attribute to extract Twitter data from developing and developed countries. Moreover, the model is general enough to be used on various industries’ supply chains and provides managers and policymakers with deep insight into reverse logistics’ decision-making. It would be interesting to use real-time analytics and improve accuracy in future works. We made original contributions to reverse logistics decision-making in the circular economy context. Previous research, which has focused on supply chain decision-making, has been extended by providing theoretical and practical implications for social media analytics and the circular economy ecosystem. Thus, by scrutinising the consumers’ needs and expectations, we suggested the best decision on returned products to close an open-ended supply chain and achieve a circular economy. Furthermore, we derived industry benchmarks for both developing and developed countries separately. The results showed that the best decision on returning products in developing countries is different from developed countries. We advise top managers and policymakers to improve supply chain sustainability using social media analytics in developing and developed countries to substantially optimise waste and companies’ profits.
  • The bright side of consumers’ opinions of improving reverse logistics decisions: a social media analytic framework
    Sadra Ahmadi, Sajjad Shokouhyar, Mohammad Hossein Shahidzadeh, I. Elpiniki Papageorgiou
    International Journal of Logistics Research and Applications, 2022
    Mitigating wastes, manufacturers must make the best decisions when it comes to reusing and recycling returned products. As unsatisfactory products are not going to be bought by customers, managers would be faced with a paradoxical decision on reusing or recycling these products. The proposed framework demonstrates how to analyse positive/negative feedback from consumers to form the most effective disposition decision strategies for managers in reverse logistics by means of sentiment analysis algorithms. Applying the framework, companies will be able to extract, categorise, and analyse their consumers’ opinion and sentiment to make a strategic decision in reverse logistics to minimise returned products, waste, inventory, and cost, while maximising efficiency, profit, SC sustainability, and customer satisfaction. While the framework is broad enough to be used in different industries, such as the electronics and automobile, the probability of biased opinion that may arises by limitation in considering a specific language or location has been greatly reduced. This paper focuses on social media data to optimise the decision-making process in reverse logistics through a big data analysis approach. In this research, a case study was conducted on Apple mobile phones Twitter data, including models and features.

RECENT SCHOLAR PUBLICATIONS

  • Revolutionizing reverse supply chain decision-making: Deep social media analysis in qualitative comparative analysis
    MH Shahidzadeh, S Shokouhyar
    Computers & Industrial Engineering 206, 111241 , 2025
    2025.0
    Citations: 3
  • Revealing the sustainable consumption barriers based on the product-service system: social media analytics approach
    A Pourranjbar, S Shokouhyar, MH Shahidzadeh, E Nikookar, ...
    Industrial Management & Data Systems 124 (12), 3240-3273 , 2024
    2024.0
    Citations: 8
  • Mastering supply chain’s decision-making establishing SDG’s goal: A social media analytics study of the electronic devices in developing and developed countries
    S Shokouhyar, MH Shahidzadeh
    Annals of Operations Research, 1-48 , 2024
    2024.0
    Citations: 5
  • Unveiling just-in-time decision support system using social-media analytics: A case-study on reverse logistics resource recycling
    SS Mohammad Hossein Shahidzadeh
    Industrial Management & Data Systems , 2024
    2024.0
    Citations: 24
  • Discovering the secret behind managing WEEE: deep learning method in industry 4.0
    MH Shahidzadeh, S Shokouhyar
    Annals of Operations Research , 2023
    2023.0
    Citations: 11
  • Shedding light on the reverse logistics’ decision-making: a social-media analytics study of the electronics industry in developing vs developed countries
    MH Shahidzadeh, S Shokouhyar
    International Journal of Sustainable Engineering 15 (1), 161-176 , 2022
    2022.0
    Citations: 41
  • Unscramble social media power for waste management: A multilayer deep learning approach
    MH Shahidzadeh, S Shokouhyar
    Journal of Cleaner Production , 2022
    2022.0
    Citations: 36
  • The bright side of consumers’ opinions of improving reverse logistics decisions: a social media analytic framework
    S Ahmadi, S Shokouhyar, MH Shahidzadeh, I Elpiniki Papageorgiou
    International Journal of Logistics Research and Applications 25 (6), 977-1010 , 2022
    2022.0
    Citations: 49
  • Toward the closed-loop sustainability development model: a reverse logistics multi-criteria decision-making analysis
    MH Shahidzadeh, S Shokouhyar
    Environment, Development and Sustainability , 2022
    2022.0
    Citations: 58
  • Demystifying Consumer's Behaviour Toward Used Electronics Handheld Devices: A Hybrid Method of Bwm-and-Deep Learning in Social Media
    MH Shahidzadeh, S Shokouhyar, E Ashrafi Mohabadi
    Available at SSRN 4524205 , 0

MOST CITED SCHOLAR PUBLICATIONS

  • Toward the closed-loop sustainability development model: a reverse logistics multi-criteria decision-making analysis
    MH Shahidzadeh, S Shokouhyar
    Environment, Development and Sustainability , 2022
    2022.0
    Citations: 58
  • The bright side of consumers’ opinions of improving reverse logistics decisions: a social media analytic framework
    S Ahmadi, S Shokouhyar, MH Shahidzadeh, I Elpiniki Papageorgiou
    International Journal of Logistics Research and Applications 25 (6), 977-1010 , 2022
    2022.0
    Citations: 49
  • Shedding light on the reverse logistics’ decision-making: a social-media analytics study of the electronics industry in developing vs developed countries
    MH Shahidzadeh, S Shokouhyar
    International Journal of Sustainable Engineering 15 (1), 161-176 , 2022
    2022.0
    Citations: 41
  • Unscramble social media power for waste management: A multilayer deep learning approach
    MH Shahidzadeh, S Shokouhyar
    Journal of Cleaner Production , 2022
    2022.0
    Citations: 36
  • Unveiling just-in-time decision support system using social-media analytics: A case-study on reverse logistics resource recycling
    SS Mohammad Hossein Shahidzadeh
    Industrial Management & Data Systems , 2024
    2024.0
    Citations: 24
  • Discovering the secret behind managing WEEE: deep learning method in industry 4.0
    MH Shahidzadeh, S Shokouhyar
    Annals of Operations Research , 2023
    2023.0
    Citations: 11
  • Revealing the sustainable consumption barriers based on the product-service system: social media analytics approach
    A Pourranjbar, S Shokouhyar, MH Shahidzadeh, E Nikookar, ...
    Industrial Management & Data Systems 124 (12), 3240-3273 , 2024
    2024.0
    Citations: 8
  • Mastering supply chain’s decision-making establishing SDG’s goal: A social media analytics study of the electronic devices in developing and developed countries
    S Shokouhyar, MH Shahidzadeh
    Annals of Operations Research, 1-48 , 2024
    2024.0
    Citations: 5
  • Revolutionizing reverse supply chain decision-making: Deep social media analysis in qualitative comparative analysis
    MH Shahidzadeh, S Shokouhyar
    Computers & Industrial Engineering 206, 111241 , 2025
    2025.0
    Citations: 3
  • Demystifying Consumer's Behaviour Toward Used Electronics Handheld Devices: A Hybrid Method of Bwm-and-Deep Learning in Social Media
    MH Shahidzadeh, S Shokouhyar, E Ashrafi Mohabadi
    Available at SSRN 4524205 , 0

CONSULTANCY

Business Analytics; Supply Chain Management; Big Data Analytics; Sustainability; Circular Economy