Knowledge Economy Studies

Abstract Recent research indicates that despite extensive investments in artificial intelligence, many organizations have yet to fully leverage its potential in strategic decision-making. This study integrates fragmented evidence on the role of artificial intelligence across the stages of strategic decision-making and develops a conceptual framework based on a systems approach encompassing input, process, and output stages. Using a structured literature review, 55 articles published between 2019 and 2025 were retrieved from Scopus, Web of Science, and Google Scholar and analyzed through Braun and Clarke's (2006) six-phase thematic analysis, supported by MAXQDA 2024. The findings indicate that, at the input stage, artificial intelligence enhances the quality of decision-making inputs through predictive analysis, data processing, strategic intelligence enhancement, and key stakeholder analysis. At the process stage, automation, risk identification, scenario modeling, enhanced decision rationality, and resource optimization improve decision-making efficiency and agility. At the output stage, improved business performance, adaptive and innovative strategy execution, strategic performance evaluation, and data-driven competitive advantage were identified. In addition, organizational, human, ethical, legal, data-related, privacy, security, and transparency-related challenges emerged as cross-cutting conditions that may influence all three levels of the decision-making cycle. The study's contribution lies in developing an integrated systems framework that links inputs, processes, and outputs while accounting for feedback among these levels.

Abstract Ornamental houseplants and home greenhouses, as part of urban agriculture and indoor cultivation, are playing a growing role in environmental sustainability, household food security, indoor air quality, and urban well-being. This review asks how far the existing literature has actually moved beyond reactive disease detection toward a complete cycle of intelligent and sustainable plant protection — one that also covers early prediction, treatment decision support, and deployment in users' real homes. Following PRISMA 2020, we analyzed 55 primary studies published between 2015 and 2025. Thematic coding produced 23 codes across 9 categories and 4 dimensions: disease detection (64%), prediction and early warning (18%), treatment support (9%), and home/greenhouse deployment (7%). Reported accuracy reaches 95–99% in laboratory settings but falls to 70–85% once models are tested in real environments. We identified five structural gaps: a strong bias toward reactive over preventive approaches, a persistent laboratory-to-reality accuracy gap, an almost complete absence of data for indoor ornamental species, no use whatsoever of large language models for treatment guidance, and a weak theoretical connection to sustainable agriculture and Integrated Pest Management (IPM) principles. Future work should prioritize lightweight, generalizable models that integrate with environmental sensing under an IPM framework, thereby translating technical accuracy into measurable sustainability outcomes.

Abstract This study examines the impact of frugal innovation (FI) on new product development performance (NPDP) within the small electrical household appliances (SEHA) sector. Additionally, it explores the moderating roles of Industry 4.0 (I4.0) technologies and technological turbulence (TT) in linking FI to NPDP. Grounded in the Resource-Based View, Human-Organization-Technology framework, and Schumpeterian innovation theory, this research, from a paradigmatic perspective, falls under post-positivism. In terms of approach and reasoning, it is quantitative-deductive, and from the perspective of method and nature, it is descriptive of the survey type. Data were collected from 111 senior managers in the Iranian SEHA industry. The responses were analyzed using PLS-SEM via Smart-PLS software. Results confirm that FI significantly and positively influences NPDP. Furthermore, the study finds that I4.0 technologies strengthen this relationship by enabling precise engineering, rapid prototyping, and cost-effective production, thus enhancing the benefits of FI under varying technological conditions. Technological turbulence also moderates the impact of I4.0, amplifying the positive effects on NPDP by fostering organizational agility and responsiveness to rapid change. The importance-performance map analysis highlights I4.0 adoption as a critical area for managerial focus to sustain competitive advantage. These findings advance the theoretical understanding of innovation management by clarifying the interplay between frugal innovation, Industry 4.0 technologies, and technological turbulence, enriching the understanding of their joint effects on NPDP. Practically, the results guide managers in resource allocation and strategic adoption of digital technologies to enhance innovation outcomes amid technological turbulence, thereby supporting sustained competitive advantage in resource-constrained and dynamic market environments.

Abstract With the rapid advancement of technology and the expanding use of artificial intelligence (AI), extensive opportunities have emerged that are expected to fundamentally reshape orientations and approaches to educational practices in higher education, while simultaneously generating new challenges. Accordingly, examining the opportunities and challenges associated with the application of AI in higher education is of critical importance for developing appropriate strategies for its effective integration into educational processes. In this context, academics, as key agents in the implementation of teaching and learning activities, are well positioned to identify both the potentials and the limitations of this technology. Therefore, the present study aimed to explore academics’ attitudes toward the opportunities and challenges of using AI in education. This study adopted a qualitative research approach using a deductive qualitative content analysis strategy. Data were collected through semi-structured interviews. The study population consisted of faculty members and postgraduate students, who were selected using purposive and convenience sampling. Sample size was determined based on theoretical data saturation. The collected data were analyzed through systematic coding procedures. The findings revealed that, from the perspectives of faculty members and students, the opportunities and challenges of AI use in education could be categorized into three dimensions-affective, cognitive, and behavioral—based on the ABC attitude model; additionally, distinctions were identified across different educational contexts as reflected in the interview data. Overall, the results indicate that identifying academics’ attitudes toward the opportunities and challenges of AI in education constitutes a fundamental step toward the informed, responsible, and effective utilization

Abstract Nowadays, in organizations, selecting the appropriate knowledge management (KM) technology has become one of the senior managers’ concerns in the KM area. The purpose of this study is to provide a model to help decision-makers with identifying and selecting the most appropriate KM technologies in‌ an ‌Iranian bank. In this research, KM technologies and criteria to select them are identified and are classified by doing librarian studies. Two questionnaire surveys were conducted to weigh technology selection criteria and determining the value of technologies based on criteria among KM experts of bank. The weight of each criterion was assigned by applying the analytical hierarchy process (AHP) and finally, all the technologies were ranked by using the PROMETHEE II method. The results indicate that in top rank is networking technology followed by organizational group communication/private social network technologies, social networking technologies, web/multimedia presenting technologies, web conferencing technologies, large audience webinars technologies, communications and collaborations technologies, virtual three-dimensional immersive collaboration technologies, and finally groupware.

Abstract The present study investigates the impact of the digital economy on the green development of the sports industry, with a focus on the moderating role of data marketability. Using annual data from 31 Iranian provinces over the period 2012–2025 (1391–1404), the study employs a Panel Quantile Vector Autoregression (PQVAR) methodology and dynamic spillover network analysis. The results from correlation and dynamic spillover analyses are consistent with a positive association between the digital economy and green development of the sports industry. Data marketability appears to play a supporting role in this relationship. Spillover analysis across the 25th, 50th, and 75th quantiles reveals that the effects are more pronounced during crisis conditions (lower quantile) compared to normal and boom periods. The Total Connectedness Index (TCI) exceeds 93% across all quantiles, confirming high systemic interdependence. Provinces such as Zanjan, Isfahan, and Qom are identified as net transmitters, while Alborz and Ardabil serve as primary receivers. Robustness checks confirm the stability of the findings. The study concludes that investments in digital infrastructure and improvements in data marketability can significantly contribute to sustainable development in the sports sector across Iranian provinces.