Management Research in Iran

Abstract Data Envelopment Analysis (DEA), as a non-parametric and data-driven methodology, is an effective tool for assessing the relative efficiency of a set of decision-making units (DMUs) with multiple inputs and outputs. However, classical DEA models disregard the internal structure of DMUs and treat them as “black boxes.” To overcome this limitation, network DEA has been developed to explicitly model the internal interactions among the components of each DMU. Moreover, in many real-world applications, certain input, output, or intermediate variables are inherently discrete and take integer values, and negative data may also be present. Such characteristics are typically ignored in conventional DEA models and can lead to inaccurate or impractical efficiency estimates. This paper proposes a novel network DEA model based on the directional distance function for two-division series systems, capable of simultaneously handling negative data and integer-valued variables. By defining an appropriate direction vector, the proposed model enables the measurement of system efficiency under both constant returns to scale and variable returns to scale. To demonstrate the capabilities of the proposed model, a real case study involving 29 Iranian supply chains in the medical consumables industry is conducted. The results show that the proposed model not only accurately distinguishes between efficient and inefficient DMUs but also provides projection points that specify the exact pathway for performance improvement in each division. The findings indicate that the proposed approach can serve as a robust and reliable tool for evaluating and enhancing supply chain efficiency, particularly in sensitive industries such as medical consumables.

Abstract The concept of strategizing, as a contemporary and applied notion in strategic management, is characterized by conceptual ambiguity and limited exploration in terms of its definition and dimensions. This study aims to clarify the concept of strategizing and reduce existing ambiguities in the literature by employing Suddaby’s conceptual analysis framework. To this end, research articles published between 1990 and July 21, 2025, were retrieved and analyzed from the Scopus database using the keywords “strategizing” and “strategy-as-practice”. The findings include: a clear definition of strategizing, specification of its analytical levels, temporal scope, and value foundations, comparison with related concepts, and the provision of a coherent framework for the concept. The proposed conceptual framework facilitates the effective application of the strategizing concept at the business, corporate, and ecosystem levels. This research contributes to the conceptual development of strategizing and serves as a foundation for future studies, particularly those exploring the required capabilities for strategizing and the mechanisms for its implementation across different organizational and managerial levels.

Abstract This study aims to explore and analyze the components and categories of enhancing innovation capacity through the application of the blue ocean strategy in the Iraqi pharmaceutical industry. Conducted as an applied research project, it employs a qualitative approach using thematic analysis. Data were gathered through semi-structured interviews with 19 managers and experts in marketing and the pharmaceutical sector, analyzed across three coding phases: open, axial, and selective. During the open coding phase, 77 initial codes were identified and grouped into 12 axial categories: red ocean challenges, the necessity to move beyond traditional competition, advantages of the blue ocean strategy, elimination of unnecessary factors, reduction of low-value factors, enhancement of existing factors, creation of new factors, expansion of innovation capacity, integration of emerging technologies, sustainability and long-term impacts, and strategic foresight. These categories were subsequently consolidated into four overarching themes: challenges, tools, applications, and outcomes. The findings reveal that competitive challenges characteristic of red ocean environments, such as intense traditional rivalry and innovation limitations, can be effectively addressed through the four-pronged framework of elimination, reduction, enhancement, and creation. By fostering new markets and incorporating advanced technologies, the blue ocean strategy enhances innovation capacity and economic sustainability, promotes transformative innovation, and establishes a enduring competitive edge.

Abstract Chatbots, as one of the important applications of artificial intelligence, are increasingly used in interactions between customers and organizations and play an important role in improving customer experience. Despite the widespread use of this technology, systematic understanding of users' expectations of chatbots, especially in the Iranian cultural context, remains limited. The aim of the present study is to identify and explain Iranian customers' expectations of interacting with chatbots in online environments. This study was conducted with a qualitative approach and using the theme analysis method. Data were collected through semi-structured interviews with 20 users with practical experience or familiarity with chatbots, and sampling was continued using the snowball method until theoretical saturation was achieved. In the data analysis process, 109 basic themes were extracted and classified into 23 organizing themes and 6 overarching themes. The findings show that customers' expectations of chatbots can be explained in six main dimensions, including effectiveness, quality of response, humanness, communication, security, and accessibility. These dimensions encompass a range of functional, interactive, and emotional expectations of users and indicate that customers, in addition to the speed and accuracy of responses, also pay special attention to features such as empathy, understanding the context of the conversation, personalization of interaction, and privacy. The results of this research can provide a practical basis for the design, development, and improvement of service chatbots tailored to the needs and characteristics of Iranian users.

Abstract Refurbished electronics such as smartphones and laptops are increasingly positioned as sustainable and affordable options amid rising e-waste and resource constraints, yet consumer acceptance in Iran remains insufficiently understood. This study integrates Ajzen’s Theory of Planned Behavior and Schwartz’s Norm Activation framework to propose a prediction-oriented model based on artificial neural networks (ANN) for estimating purchase intention and behavior and for quantifying the relative contribution of theoretical constructs. The research is descriptive–applied, survey-based, and cross-sectional; data were collected from 400 Iranian consumers of smartphones and laptops in Fall–Winter 2024 using convenience sampling and a five-point questionnaire. After data cleaning, reverse-coding of negatively worded items, and min–max normalization, multilayer ANNs were trained in MATLAB with a 70/15/15 split for training/validation/testing. Beyond prediction, variable importance was derived via sensitivity analysis. Results indicate that the ANN provides reliable out-of-sample prediction for both intention and behavior. In the ranking of drivers, perceived behavioral control and attitude toward the behavior contribute most to explaining purchase behavior, while purchase intention remains an effective bridge from cognitive constructs to actual behavior. Subjective norms, personal norms (morality), and awareness of consequences exert positive but more modest effects. The learning-based approach offers a finer-grained picture of latent patterns than linear explanations and yields a more dependable estimate of purchase likelihood in the Iranian context. Managerially, effective policies should jointly strengthen perceived capability and ease of action (e.g., simpler purchase processes and lower after-sales risk) and build favorable attitudes grounded in economic and environmental benefits to increase acceptance of

Abstract Two primary approaches exist for empirical tests assessing the efficiency of options contract markets: the model-based approach and the arbitrage-based approach. Numerous empirical studies have examined model-based efficiency (comparing model-derived option prices with market prices). Still, they have yielded conflicting results regarding the efficiency of the options market, with some studies supporting market efficiency and others rejecting it. This study conducts a meta-analysis of options market efficiency under the model-based approach to reconcile these contradictions and identify their underlying causes. We selected 30 studies published between 2003 and 2022 in SCOPUS-indexed journals, extracting 6,409 test samples for hypothesis testing. The sample includes all reported tests in prior empirical studies (published in SCOPUS-indexed journals) on model-based options market efficiency. Our findings reject market efficiency, indicating a significant divergence between option contract prices and their intrinsic values. Robustness tests—including efficiency metrics, option pricing models, journal H index, analysis date, and moneyness depth—consistently confirm this conclusion.