Creative Industries and Entrepreneurship
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Browsing Creative Industries and Entrepreneurship by Author "Anwar, Imran"
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Item Brand expertise, impulsiveness and materialism aggravate unhealthy food products buying among young adults despite pricing and sin tax interventions(Cogent OA, 2024) Thayyib P.V.; Anwar, Imran; M. M, Sulphey; Yasin, Naveed; Thabit Yahya, AliBuilding upon the stimulus-organism-response (S-O-R) and the hedonic motivation theories, this study aims to assess the effects of consumer awareness variables viz. unhealthy product knowledge (PK), brand expertise (BE), perceived price and tax policy interventions (PTP), and personality traits variables vz. materialism (MT) and buying impulsiveness (BI) on consumers’ purchase intention toward unhealthy products. The study used a between-subjects experimental design to form control (n = 341) and experiment (n = 355) groups before treating the experiment group with health warnings and persuasive audio-visual commercials. After stimuli creation, both groups were asked to fill a questionnaire. We employed CB-SEM in AMOS v.24.0 to assess the model’s global fit indices, reliability and validity, hypotheses testing. The results affirm that the model meet the criteria of global fit indices and meet the assumptions of reliability (unidimensionality of the scales) and validity (convergence and divergence). Further, the results of hypotheses testing show that BE, MT, and BI increase purchase intention, demonstrating that hedonic motivations prevalent in youngsters override health warnings. Surprisingly, PTP and PK do not appear to influence purchasing intent, reinforcing impulsive buying and materialistic personality traits of respondents. The findings imply that companies counterbalance statutory health warnings with attractive advertising. Because PTP and PK have little effect on purchase intentions, the government can maximize revenue by taxing unhealthy products, thereby protecting public health. The findings provide valuable insights into consumer behavior for marketing academics, retailers, consumer marketing companies, and indirect tax policymakers. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.Item Role of entrepreneurship education, passion and motivation in augmenting Omani students’ entrepreneurial intention: A stimulus-organism-response approach(Elsevier Ltd, 2023-11) Anwar, Imran; Ahmad, Alam; Saleem, Imran; Yasin, NaveedItem Savior or Distraction for Survival: Examining the Applicability of Machine Learning for Rural Family Farms in the United Arab Emirates(MDPI, 2023-02) Gilani, Sayed Abdul Majid; Copiaco, Abigail; Gernal, Liza; Yasin, Naveed; Nair, Gayatri; Anwar, ImranMachine learning (ML) has seen a substantial increase in its role in improving operations for staff and customers in different industries. However, there appears to be a somewhat limited adoption of ML by farm businesses, highlighted by a review of the literature investigating innovative behaviors by rural businesses. A review of the literature identified a dearth of studies investigating ML adoption by farm businesses in rural regions of the United Arab Emirates (UAE), especially in the context of family-owned farms. Therefore, this paper aims to investigate the drivers and barriers to ML adoption by family/non-family-owned farms in rural UAE. The key research questions are (1) what are the drivers and barriers for rural UAE farms adopting ML? As well as (2) is there a difference in the drivers and barriers between family and non-family-owned farms? Twenty semi-structured interviews were conducted with farm businesses across several rural regions in the UAE. Then, through a Template Analysis (TA), drivers and barriers for rural UAE-based farm owners adopting ML were identified. Interview findings highlighted that farms could benefit from adopting ML in daily operations to save costs and improve efficiency. However, 16 of 20 farms were unaware of the benefits related to ML due to access issues (highlighted by 12 farms) in incorporating ML operations, where they felt that incorporating ML into their operations was costly (identified by 8 farms). It was also identified that non-family-owned farms were more likely to take up ML, which was attributed to local culture influencing family farms (11 farms identified culture as a barrier). This study makes a theoretical contribution by proposing the Machine Learning Adoption Framework (MLAF). In terms of practical implications, this study proposes an ML program specifically targeting the needs of farm owners in rural UAE. Policy-based implications are addressed by the findings aligning with the United Nations’ Sustainability Development Goals 9 (Industry, Innovation, and Infrastructure) and 11 (Sustainable Cities and Communities). © 2023 by the authors.