Al Akhawayn University

Halhoul Merabet G., Essaaidi M., Ben Haddou M., Qolomany B., Qadir J., Anan M., Al-Fuqaha A., Abid M.R., Benhaddou D.

Building operations represent a significant percentage of the total primary energy consumed in most countries due to the proliferation of Heating, Ventilation and Air-Conditioning (HVAC) installations in response to the growing demand for improved thermal comfort. Reducing the associated energy consumption while maintaining comfortable conditions in buildings are conflicting objectives and represent a typical optimization problem that requires intelligent system design. Over the last decade, different methodologies based on the Artificial Intelligence (AI) techniques have been deployed to find the sweet spot between energy use in HVAC systems and suitable indoor comfort levels to the occupants. This paper performs a comprehensive and an in-depth systematic review of AI-based techniques used for building control systems by assessing the outputs of these techniques, and their implementations in the reviewed works, as well as investigating their abilities to improve the energy-efficiency, while maintaining thermal comfort conditions. This enables a holistic view of (1) the complexities of delivering thermal comfort to users inside buildings in an energy-efficient way, and (2) the associated bibliographic material to assist researchers and experts in the field in tackling such a challenge. Among the 20 AI tools developed for both energy consumption and comfort control, functions such as identification and recognition patterns, optimization, predictive control. Based on the findings of this work, the application of AI technology in building control is a promising area of research and still an ongoing, i.e., the performance of AI-based control is not yet completely satisfactory. This is mainly due in part to the fact that these algorithms usually need a large amount of high-quality real-world data, which is lacking in the building or, more precisely, the energy sector. Based on the current study, from 1993 to 2020, the application of AI techniques and personalized comfort models has enabled energy savings on average between 21.81 and 44.36%, and comfort improvement on average between 21.67 and 85.77%. Finally, this paper discusses the challenges faced in the use of AI for energy productivity and comfort improvement, and opens main future directions in relation with AI-based building control systems for human comfort and energy-efficiency management. • One of the first systematic reviews of thermal comfort with individual interactions into comfort energy control loop. • A holistic view of the complexities of delivering thermal comfort in buildings in an energy efficient way. • AI/ML technology implementation in building industry is still an ongoing endeavor. • Discussion on research challenges facing AI-based modeling in buildings which is due to lack of data.

Ikram M., Ferasso M., Sroufe R., Zhang Q.

The United Nations proposed the Sustainable Development Goals (SDGs) to foster sustainable development, including tackling environmental challenges and adopting cleaner production practices worldwide. Green technology is crucial for the implementation of the SDGs. Currently, there is an urgent need to form a long-term sustainable investment mechanism for screening, evaluation, and promotion of appropriate green technology. Therefore, this study develops an integrated green technology framework to fill a gap in the literature by prioritizing green technologies' most critical attributes in Pakistan. Initially, we focus on eight leading green technology indicators: Environmental Quality, Resource Utilization, Agriculture & forestry, Green building, Energy utilization, Green Transport, Life Health, and Ecology Safety with the help of the fuzzy Delphi method. Fuzzy Analytical Hierarchy Process (FAHP) is employed to find primary and sub-indicators relative importance. Results reveal that Energy Utilization and agriculture & forestry are significant indicators. Supply chain and sustainable food security, Energy Recycling and Eco-Farming obtained the highest weight scores and are seen as essential among 43 sub-criteria. This study is among the first to assess the green technology indicators for cleaner production and sustainable investment to achieve sustainable development. This study's outcomes can help scholars, managers, government agencies, and decision-makers understand the importance of green technologies to achieve SDGs while simultaneously improving sustainability practices.

Chetioui Y., Benlafqih H., Lebdaoui H.

PurposeThis study examines the impact of attitudes toward fashion influencers (FIs) on brand attitude and consumer purchase intention. It also aims to identify factors affecting consumers' attitudes toward FIs.Design/methodology/approachTo achieve this goal, the authors propose a conceptual model that combines the theory of planned behavior (TPB) and theoretical outcomes of prior literature related to influencer marketing. Based on data collected from 610 Moroccan respondents, the authors empirically test the conceptual model using a partial least squares (PLS) estimation.FindingsThis study illustrates that attitudes toward FIs positively impact brand attitude and consumer purchase intention. The authors also demonstrate that perceived credibility, trust, perceived behavioral control, perceived subjective norms, perceived expertise and perceived congruence positively impact attitudes toward FIs.Practical implicationsThe study findings help marketers and advertisers in the fashion industry to understand how influencer marketing contributes to consumer purchase intention. They also allow marketers to understand factors explaining attitudes toward FIs and therefore better select influencers capable of creating purchase intentions among existing and potential customers.Originality/valueThe present paper bridges a gap pertaining to antecedents and factors that impact attitudes toward FIs and consumer purchase intention. To the authors’ knowledge, this study is the first of its kind to investigate the impact of attitudes toward influencers on both brand attitude and purchase intention in the fashion industry.

Mutombo P.N., Fallah M.P., Munodawafa D., Kabel A., Houeto D., Goronga T., Mweemba O., Balance G., Onya H., Kamba R.S., Chipimo M., Kayembe J.N., Akanmori B.

Ali S.M., Appolloni A., Cavallaro F., D’Adamo I., Di Vaio A., Ferella F., Gastaldi M., Ikram M., Kumar N.M., Martin M.A., Nizami A., Ozturk I., Riccardi M.P., Rosa P., Santibanez Gonzalez E., et. al.

Sustainability’s growth, year after year, continues to be staggering, becoming a reference point for those working on these issues [...]

Ashaari M.A., Singh K.S., Abbasi G.A., Amran A., Liebana-Cabanillas F.J.

• Deep learning provides higher accuracy than structural equation modelling. • Proposed integrated model provides useful guidelines to higher education institutes. • Multi-analytical methods provide in-depth knowledge of data-driven decision-making. Despite the growing interest towards big data within higher education institutions (HEI), research on big data analytics capability within the HEI context is somewhat limited. This study's main objective is to have a better understanding of the utilisation of big data analytics capability for data-driven decision-making to achieve better performance from Malaysian HEIs. Despite the growing interest towards big data within higher education institutions (HEI), research on big data analytics capability within the HEI context is rather limited. This study's main objective is to have a better understanding of the utilisation of big data analytics capability for data-driven decision-making to achieve better performance from Malaysian HEIs. This study validates an integrative model by combining information processing theory and resource-based view theory. Unlike extant literature, this study proposed methodology involving dual-stage analysis involving of Partial Least Squares Structural Equation Modelling and evolving Artificial Intelligence named deep learning (Artificial Neural Network) were performed. The application of deep ANN architecture can predict 83% of accuracy for the proposed model. Besides, the outcome of data-driven decision making from the relationship between big data analytic capability and data-driven decision making towards the performance of HEIs has significant findings. Results revealed that data-driven decision making could positively play an essential role in the relationship between big data analytic capability and performance of HEIs. Theoretically, the newly integrated theoretical model that incorporates information processing theory and resource-based view provides useful guidelines to HEI's about the crucial capabilities and resources that must be put into place to reap the benefits associated with big data implementations in the wake of Industry Revolution 4.0.

Bourhnane S., Abid M.R., Lghoul R., Zine-Dine K., Elkamoun N., Benhaddou D.

Predicting energy consumption in Smart Buildings (SB), and scheduling it, is crucial for deploying Energy-efficient Management Systems. Most important, this constitutes a key aspect in the promising Smart Grids technology, whereby loads need to be predicted and scheduled in real-time to cope for the strongly coupled variance between energy demand and cost. Several approaches and models have been adopted for energy consumption prediction and scheduling. In this paper, we investigated available models and opted for machine learning. Namely, we use Artificial Neural Networks (ANN) along with Genetic Algorithms. We deployed our models in a real-world SB testbed. We used CompactRIO for ANN implementation. The proposed models are trained and validated using real-world data collected from a PV installation along with SB electrical appliances. Though our model exhibited a modest prediction accuracy, which is due to the small size of the data set, we strongly recommend our model as a blue-print for researchers willing to deploy real-world SB testbeds and investigate machine learning as a promising venue for energy consumption prediction and scheduling.

Jum'a L., Zimon D., Ikram M., Madzík P.

Sustainable development has become a prerequisite for any organization that ensure environmental, economic, and social sustainability during the production process. However, how lean manufacturing helps to achieve each of the three dimensions of sustainability was not adequately considered by prior studies, since one type of sustainability or the concept of sustainability as a whole has been investigated. Moreover, few studies investigated the combined effect of lean practices and Sustainability-Oriented Innovation (SOI) on each type of sustainability. Therefore, this study aims to investigate the impact of lean manufacturing practices on Triple Bottom Line (TBL) by taking SOI as a mediator. Data of 392 managers from different manufacturing organizations in Jordan was collected through well-structured questionnaire. Structural Equation Modeling (SEM) was employed to check the relationship between latent and observed variables. The results reveal that both lean practices and SOI has significant and positive effect on TBL. Although lean manufacturing focus on efficiency while sustainability-oriented innovation promotes responsiveness to customers. Besides, SOI plays a partial mediating role between lean manufacturing and sustainable development. This study provides evidence that either lean practices or SOI and their combination significantly assure sustainability. The findings will assist decision-makers, supply chain managers and academicians to understand the importance of lean practices and SOI to achieve sustainability. • Lean practices or SOI and their combination significantly assure sustainability. • Lean practices have the highest influence on social sustainability. • Sustainability-oriented innovation has the highest influence on economic sustainability.

Abid N., Ceci F., Ikram M.

Over the last two decades, the phenomenon of green growth has gained much attention from academics and policymakers striving to find a sustainable solution to environmental problems worldwide. Technological innovation serves as a tool to abate the acute environmental crisis and continuously promote sustainable development by converting traditional economies into green economies. Pakistan is among the developing countries relying on conventional technology and energy resources to meet population and economy demands, which has led to a surge in greenhouse gases and other hazardous air pollutants. The literature exploring green growth in the Pakistani context is scant; the present study will therefore fill this gap and explore the dynamic linkage between technological innovation and ISO 14001 with green growth in Pakistan in the presence of environmental challenges such as energy consumption and population growth. A novel grey relational analysis model approach is employed to examine the interrelationship between the study parameters. Results indicate that technological innovation is significantly correlated with green growth, and ISO 14001 also shows a substantial relationship with green growth. However, among the environmental challenges, energy consumption poses a barrier to green growth development as the country’s energy mix is dominated by fossil fuels as compared to renewables. The research findings produce a much-needed policy suggestion to address the environmental challenges by promoting green growth development in Pakistan.

Le T.T., Ikram M.

This study unfolds the nexus between sustainability innovation, firm performance of small- and medium-sized enterprises (SMEs) by exploring the mediating role of firm competitiveness in the context of an emerging market. This study adopts a systematic literature review approach which results in arguing that this relationship is not explored in-depth in current literature. This is especially more critical for emerging economies as current literature shows a lack of empirical research in this research topic. A well-structured questionnaire was administrated for data collection. A total of 435 valid responses from top executives, managers, and experts were received and used for data analysis. Structural equation modeling (SEM) was used to investigate the relationships between constructs and latent variables. Our findings reveal that there is a significant and positive relationship among sustainability innovation and firm competitiveness. Firm competitiveness has a positive and significant relationship with financial, environmental, and operational performance. Moreover, sustainability innovation had an indirect positive and significant effect on financial performance. The statistical results indicate that the indirect effects are all significant (sig. < 0.05) and the order of these indirect effects is consistent with those of direct effects of firm competitiveness on firm performance dimensions. Stakeholder theory is applied to check how firm competitiveness helps improve the performance of SMEs. To the best of our knowledge this study is the first that explores the mediating role of firm competitiveness between sustainability innovation and SMEs’ performance. Based on the context of the influence of the COVID-19 pandemic, this study presents implications for entrepreneurs and top management with respect to strategic perspectives to drive their businesses in a sustainable direction.