Impact of Approval Goals and Motivation on Consumer Intention: A Retail Context

Muhammad Farooq AKHTAR(Muhammad Farooq AKHTAR ),Norazah Mohd SUKI(Norazah Mohd SUKI ) 한국유통과학회 2022 유통과학연구 Vol.20 No.12

Purpose: The objective of the study is to examine the role of approval goals, subjective norm, internal motivation, external motivation, attitude towards behavior, and perceived behavioral control on retail consumer’s intention to consume fortified food in Pakistan. Research design, data, and methodology: The study was quantitative in nature. That is why the data were collected from 384 respondents approaching retail stores of Lahore, Gujranwala, and Faisalabad using mall intercept survey. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the data. Results: The results show that approval goals significantly influence subjective norms. Secondly, subjective norms positively influence internal and external motivation. Thirdly, attitude towards behavior and internal motivation significantly impacted on intention. However, the findings of the study show, non-significant relationship of external motivation and perceived behavioral control with intention to consume fortified food. Conclusion: Theory of reasoned goal pursuit was used to investigate consumer intention to consume fortified food in Pakistan. This study is helpful for the marketers to create a word-of-mouth strategy to enhance positive word of mouth for the company, which ultimately beneficial to develop the distribution strategy of the firm. Fortified food is full of health enriched ingredients which is beneficial for society at large.

An Application of Machine Learning in Retail for Demand Forecasting

Muhammad Umer Farooq,Mustafa Latif,Waseem,Mirza Adnan Baig,Muhammad Ali Akhtar,Nuzhat Sana International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.8

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

An Application of Machine Learning in Retail for Demand Forecasting

Muhammad Umer Farooq,Mustafa Latif,Waseemullah,Mirza Adnan Baig,Muhammad Ali Akhtar,Nuzhat Sana International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.9

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

Pressure mode decomposition analysis of the flow past a cross-flow oscillating circular cylinder

Muhammad Sufyan,Hamayun Farooq,Imran Akhtar,Zafar Bangash 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.1

Proper orthogonal decomposition (POD) is often employed in developing reduced-order models (ROM) in fluid flows for design, control, and optimization. Contrary to the usual practice where velocity field is the focus, we apply the POD analysis on the pressure field data obtained from numerical simulations of the flow past stationary and oscillating cylinders. Since pressure mainly contributes to the hydrodynamic forces acting on the structure, we compute the pressure POD modes on the cylinder surface oscillating in lock-in and lock-out regions. These modes are then dissected into sine and cosine magnitudes to estimate their contribution in the development of pressure lift and drag decomposition coefficients, respectively. The key finding of this study is that more POD modes are required to capture the flow physics in nonsynchronous regimes as compared to synchronization case. Engineering application of this study is the development of reduced-order models for effective control techniques.

Influence of an Al₂O₃ interlayer in a directly grown graphene-silicon Schottky junction solar cell

Rehman, Malik Abdul,Akhtar, Imtisal,Choi, Woosuk,Akbar, Kamran,Farooq, Ayesha,Hussain, Sajjad,Shehzad, Muhammad Arslan,Chun, Seung-Hyun,Jung, Jongwan,Seo, Yongho Elsevier 2018 Carbon Vol.132 No.-

<P><B>Abstract</B></P> <P>Graphene/Si Schottky junction solar cells are widely studied in relation to the harvesting of solar energy, but high efficiency is limited due to surface recombination at the interface. Moreover, surface defects, wrinkles, and impurities may arise during the wet transfer process of graphene. We propose an easy approach to fabricate high efficiency solar cells by using directly grown graphene on a textured substrate with a large active area. In our novel technique, we directly grow a few layers of graphene on top of Al<SUB>2</SUB>O<SUB>3</SUB>/Si by using plasma enhanced chemical vapor deposition. The high-k dielectric layer of Al<SUB>2</SUB>O<SUB>3</SUB> acts as an electron blocking layer which minimizes the surface recombination at the interface. Furthermore, the barrier width is optimized by controlling the thickness of the Al<SUB>2</SUB>O<SUB>3</SUB> interlayer to achieve the highest efficiency of 8.4%. The devices were not intentionally doped, and no aging effect was found in 9 months. We believe that our stable solar cell results indicate a new route for the production of metal-insulator-semiconductor Schottky junction solar cells with high efficiency without need of chemical doping of the emitter layer.</P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>

Functional Foods Intention: The Role of Procurement Goals in Retailing Context

Norazah Mohd SUKI,Muhammad Farooq AKHTAR,Norbayah Mohd SUKI 한국유통과학회 2023 유통과학연구 Vol.21 No.11

Thickness-dependent efficiency of directly grown graphene based solar cells

Rehman, Malik Abdul,Roy, Sanjib Baran,Akhtar, Imtisal,Bhopal, Muhammad Fahad,Choi, Woosuk,Nazir, Ghazanfar,Khan, Muhammad Farooq,Kumar, Sunil,Eom, Jonghwa,Chun, Seung-Hyun,Seo, Yongho Elsevier 2019 Carbon Vol.148 No.-

<P><B>Abstract</B></P> <P>It is of immense interest to improve the power conversion efficiency of graphene/silicon Schottky junction solar cells. The ultrathin graphene has essential properties, such as tunable work function to increase Schottky barrier height and built-in potential for efficient charge transport in photovoltaic devices. Here, we use plasma-enhanced CVD to grow graphene directly on planar n-type silicon to fabricate solar cells compatible for industrial-level applications. A key component to our accomplishment is the optimization of directly grown, continuous layers of graphene to achieve superior performance. Thus, by controlling the graphene thickness, the work function is significantly improved, the open circuit voltage is increased, and the energy conversion efficiency is enhanced. While the transfer of CVD grown graphene has limitations due to cracks and impurities during the complex process, our direct growth method demonstrates an efficiency of 5.51 % on bare planar silicon with a large device area. Furthermore, the efficiency is remarkably increased to 9.18 % by adding and doping a polymer layer. Interestingly, with the addition of a doped polymer layer, the cell exhibits excellent stability for at least one month. Our result suggests a promising simple path to fabricate high efficiency solar cells at low temperature and low cost.</P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>