International Journal of Metaverse & Virtual Transformation

Abstract This research aims to identify and evaluate an advanced tourism ecosystem management model in the Cultural Heritage and Tourism Organization of Khuzestan Province. The study is an applied-developmental one. Based on targeted sampling, 15 managers from the Cultural Heritage and Tourism Organization of Khuzestan Province were interviewed, and in the quantitative section, all heads, managers, and employees working in the organizational sections, whose number is unlimited and according to the Kohne formula is 211, were considered. The qualitative data analysis was performed using the ATLAS.ti software, and for validation, structural equation modeling methods were used in the SMARTPLS software. In the qualitative stage, the identification of 6 main themes, 26 central themes, and 160 initial codes showed that advanced tourism ecosystem management is a multidimensional, network-based phenomenon that depends on the interaction of various organizational and environmental layers. Overall, the results indicate that achieving an advanced tourism ecosystem in Khuzestan Province is not a one-time project but a gradual process requiring institutional commitment, investment in digital infrastructure, human capital empowerment, and synergy between public and private sectors. The proposed model can provide a basis for intelligent policymaking, efficient resource allocation, and enhancing the province's competitiveness at the national and international levels, paving the way for sustainable development and enhancing the status of Khuzestan tourism.

Abstract This research employs a mixed exploratory design, focusing on two complementary methods: literature synthesis and fuzzy Delphi. In the first phase, the seven-stage Sandelowski and Baruss (2007) model was utilized to extract an initial pattern of mission-oriented performance management. A systematic literature review identified 371 studies, narrowing down to 34 for final analysis after applying inclusion and exclusion criteria. Qualitative analysis of these sources yielded 237 conceptual codes, eventually organized into 17 components and 51 indicators as the initial conceptual framework of the study.
In the second phase, to refine the model and achieve expert consensus, the fuzzy Delphi method was employed. The expert panel consisted of 15 university and professional specialists in performance management and the oil industry, selected through targeted sampling. The Delphi questionnaire was administered in three rounds. Expert linguistic assessments were converted into triangular fuzzy numbers, aggregated, and defuzzified. Indicators with scores below the predefined threshold were eliminated or modified. The Kendall coefficient of concordance was calculated to measure the agreement among experts. After three rounds, a stable consensus was reached on the model's dimensions, components, and indicators, solidifying the final mission-oriented performance management model.

Abstract This research aims to identify and evaluate a native model for creating an organizational transformation culture based on new public services in smart governance and emerging technologies. This study is applied-developmental in nature. Based on purposive sampling, 15 managers of the National Iranian Oil Company were interviewed. In the quantitative section, all heads, managers, and staff working in the corporate sectors were considered; their number was unlimited, and based on Cochran’s formula, a sample size of 384 was determined. The qualitative method of Grounded Theory data analysis was used using ATLAS.ti software, and Structural Equation Modeling (SEM) in SmartPLS software was employed for validation. The results of model fit using structural equations, supported the coherence and empirical validity of the proposed model. It showed that the implementation of these strategies directly and indirectly leads to improved organizational efficiency, enhanced service quality, strengthened public trust, reduced operational costs, and the formation of competitive advantages in the public sector. Accordingly, the transformation culture in the National Iranian Oil Company is not merely a transient attitudinal change but a strategic mechanism for aligning economic missions with the values of new public services and the requirements of smart governance. This mechanism, if supported by continuous managerial and institutional backing, can pave the way for the transition of this organization into an agile, accountable, data-driven, and citizen-centric institution at both national and regional levels, laying the essential cultural foundation for the successful adoption of advanced technologies such as the Metaverse, Artificial Intelligence, and Digital Twins.

Abstract This study aims to model the impact of corporate social responsibility (CSR) on environmental sustainability through the adoption of digital technologies and smart energy management in Iranian oil and petrochemical companies. In the complex and competitive environment of the energy industry, CSR is no longer viewed merely as an ethical or legal obligation; rather, it has become a strategic approach for developing digital infrastructure, improving production processes, and enhancing energy efficiency. The statistical population includes 30 Iranian oil and petrochemical companies during the period 2016–2024. To analyze the relationships among variables and examine heterogeneous effects across different levels of energy performance, a panel quantile regression model was employed. The findings show that CSR significantly contributes to reducing energy intensity and improving environmental sustainability through the development of digital technologies, smart monitoring systems, big data analytics, the industrial Internet of Things (IIoT), and intelligent energy management systems. Companies with higher investment in digital infrastructure and smart energy management demonstrate better performance in reducing pollutant emissions, optimizing resource consumption, and improving operational efficiency. The results also indicate that the effect of CSR is stronger in larger firms and organizations with higher levels of digital maturity. Furthermore, analysis of higher energy consumption quantiles (Q75 and Q95) reveals that the role of digital technologies and smart energy management becomes more pronounced in strengthening the environmental impacts of CSR. Overall, CSR is most effective in promoting environmental sustainability when combined with digital transformation strategies and integrated energy management systems in the oil and petrochemical industry.

Abstract This study analyzes multi-objective decision-making indicators for evaluating non-dominated solutions to smart supply chain risks in the electricity industry using the Attride-Stirling thematic analysis method. The research is applied in purpose and adopts a descriptive-analytical, qualitative survey design. The expert panel consisted of 18 electricity industry professionals and academic specialists with expertise in smart supply chains and decision-making, whose insights were employed to identify, refine, and validate the emerging themes.
Data were collected through an extensive review of the relevant literature and semi-structured expert interviews. Thematic analysis was conducted following Braun and Clarke’s six-phase framework, including data familiarization, initial coding, theme generation, theme review, theme definition and naming, and report preparation. The analysis yielded 45 initial codes organized around the overarching theme of developing a multi-objective framework for evaluating non-dominated solutions to smart electricity supply chain risks. Three second-level organizing themes were identified: risk and reliability criteria, economic and cost criteria, and operational and resilience criteria. To ensure methodological rigor, Holsti’s coefficient, Scott’s pi, Cohen’s kappa, and Krippendorff’s alpha were employed to evaluate credibility and reliability, yielding values of 0.811, 0.835, 0.850, and 0.909, respectively, indicating substantial inter-coder agreement. The proposed framework provides a conceptually coherent and reliable basis for supporting multi-objective decision-making, selecting Pareto-efficient solutions, strengthening intelligent risk management, and enhancing digitally enabled supply chain resilience in the electricity industry.

Abstract The present study aimed to design a smart logistics model based on the energy internet system using Attride-Stirling’s thematic network analysis. This applied research is descriptive-analytical in nature. To identify and explain effective indicators, a qualitative approach was adopted through semi-structured interviews with 18 university professors and industry experts selected via non-random, purposive, and criterion-based sampling until theoretical saturation was achieved. Data analysis was conducted using ATLAS.ti software, strictly following Attride-Stirling’s comprehensive six-step model for thematic extraction. Initially, 120 raw codes were extracted, which were refined and merged into 32 basic themes, 10 first-level constructive themes, and 3 second-level constructive themes. Ultimately, an overarching global theme, “Smart Logistics Model Based on the Energy Internet System,” was established. Findings categorized indicators into three main areas: institutional, managerial, and social contexts; human and knowledge capacities; and technical/operational capacities. Sub-components included legal regulations, cultural factors, strategic financing, individual traits, knowledge resources, and technological infrastructure. To ensure qualitative rigor, four indices were calculated: Holsti’s coefficient (0.887), Scott’s Pi (0.755), Cohen’s Kappa (0.725), and Krippendorff’s Alpha (0.817), confirming superior validity, reliability, and consistency. Results demonstrate that achieving energy-internet-based smart logistics requires the synchronized development of technological infrastructure, knowledge capacities, and robust institutional platforms. This model serves as a comprehensive strategic framework for policy-making, planning, and the operational development of intelligent logistics systems within the evolving landscape of new energies. Such systemic integration will significantly enhance operational sustainability and logistical efficiency across various national industrial sectors and infrastructure projects.