Emadi, M., Seyfoori, E., Sobhanian, M.H., & Khoddami, S. (2023) The relationship between Green Social Media Marketing and Intention of Using Energy Saving Services based on the Theory of Planned Behavior and Norm Activation Model, Journal of Global Marketing

Under Revision, Jan 2024

Abstract:
In recent years, global warming and the consequent shortage in energy resources have urged countries and communities worldwide to take a closer look at their consumption behavior. Social Media has provided a platform to promote pro-environmental behaviors. Such pro-environmental activities and content on Social Media platforms are known as Green Social Media Marketing (GSMM). The influence of Social Media Marketing on Consumer Green Purchase Intention and Behavior has already been a controversial topic among scholars. Also, there are more obstacles slowing down the growth of Energy Service Companies (ESCO) in developing countries. Some of these constraints are due to structural malfunctions of the governments and others are affiliated to cultural norms. This paper tries to pave the way for the development of the ESCOs’ by depicting the effectiveness of GSMM and the role of goverments’ Public Policy in their markets particularly in developing countries. This study aims to explain the relationship between GSMM, Personal Norms (PN), and Consumer Green Purchase Intention using some behavioral theories such as the theory of planned behavior and the Norm Activation Model. A total of 362 questionnaires were obtained from Instagram users who were exposed to a specific page related to energy efficiency services using a convenience sampling approach. Partial Least Square Structural Equation Modelling (PLS-SEM) was employed for testing the hypotheses. The results confirmed that GSMM significantly influenced PNs. The relationship Between PNs and Green Purchase Intention as well as the mediating role of Public Policies was reported positive and significant.

Emadi, M., Ahmadi, P., & Sadatrasoul, M., Developing an information and recommendation system to save and rank building energy consumption using occupant behavior data: A combined Data Mining and MCDM approach.

Residential buildings account for a significant portion of global energy consumption, emphasizing the need for energy-saving solutions and demand-side management. Understanding occupant behavior and its impact on residential energy consumption is vital for creating effective behavior modification strategies. However, traditional simulation models fail to address this issue due to the diversity of human habitats, lifestyles, and cultures. This study develops a comprehensive approach to analyze energy consumption patterns in residential buildings, identify behavioral patterns, and recommend tailored behavior modifications using a combination of data mining and multi-criteria decision-making (MCDM) techniques. The proposed methodology consists of five main steps: feature selection, clustering, association rule mining, artificial neural networks, and entropy-based TOPSIS. The method is applied to a large dataset containing appliance-level energy consumption data for 39 Scottish buildings. Additionally, an innovative ranking system is introduced to promote energy-saving practices and stimulate competition among buildings. This research contributes to bridging the gap in understanding occupant behavior and its role in energy consumption, providing valuable insights for policymakers and stakeholders in the energy sector.

[Best paper of FRO Conference in Iran] Designing and fabrication of a 400-600mm class quadcopter for flight time enhancement by implementing an aluminium framework inspired by truss and optimizing components (in Persian), National Conference on Flying Robots (FRO), Gorgan

Abstract: