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The main goal of this research work is the optimization of the purchasing business process in the Moroccan public sector in terms of transparency and budgetary optimization. The authors have worked on the public university as an implementation field.

The design of the research work followed the design science research (DSR) methodology for information systems. DSR is a research paradigm wherein a designer answers questions relevant to human problems through the creation of innovative artifacts, thereby contributing new knowledge to the body of scientific evidence. The authors have adopted a techno-functional approach. The technical part consists of the development of an intelligent recommendation system that supports the choice of optimal information technology (IT) equipment for decision-makers. This intelligent recommendation system relies on a set of functional and business concepts, namely the Moroccan normative laws and Control Objectives for Information and Related Technology's (COBIT) guidelines in information system governance.

The modeling of business processes in public universities is established using business process model and notation (BPMN) in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature. Implementation of artificial intelligence techniques can bring great value in terms of transparency and fluidity in purchasing business process execution.

Business limitations: First, the proposed system was modeled to handle one type products, which are computer-related equipment. Hence, the authors intend to extend the model to other types of products in future works. Conversely, the system proposes optimal purchasing order and assumes that decision makers will rely on this optimal purchasing order to choose between offers. In fact, as a perspective, the authors plan to work on a complete automation of the workflow to also include vendor selection and offer validation. Technical limitations: Natural language processing (NLP) is a widely used sentiment analysis (SA) technique that enabled the authors to validate the proposed system. Even working on samples of datasets, the authors noticed NLP dependency on huge computing power. The authors intend to experiment with learning and knowledge-based SA and assess the' computing power consumption and accuracy of the analysis compared to NLP. Another technical limitation is related to the web scraping technique; in fact, the users' reviews are crucial for the authors' system. To guarantee timeliness and reliable reviews, the system has to look automatically in websites, which confront the authors with the limitations of the web scraping like the permanent changing of website structure and scraping restrictions.

The modeling of business processes in public universities is established using BPMN in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature.

The adopted techno-functional approach enabled the authors to bring information system governance from a highly abstract level to a practical implementation where the theoretical best practices and guidelines are transformed to a tangible application.

Consumer-perceived review attitude determines consumer overall information adoption and is a core part of consumer’s online-shopping. This study aims to focus on factors that could influence consumer review attitude and can be used by marketers to shape individual information perception.

The study used the questionnaire method to collect data from online shoppers and the modelling of structural equations as an empirical approach to analyse the data.

The findings demonstrate that both systematic and heuristic cues impact the reviewer’s credibility and perceived website attitude differently, which, in turn, influence review attitude. Review characteristics, such as factuality, consistency and relevancy, have a positive relationship with reviewer credibility, while only review consistency and relevancy appears to have a relationship with review attitude. Website characteristics such as reputation, familiarity and social interactivity positively influence the website attitude, which positively influences review attitude. Apart from this, review skepticism has a significant negative relationship with review attitude.

This study could help to foster a positive attitude towards online reviews. Digital marketers need to motivate trusted reviewers to post consistent, fact-based reviews. Further improving the overall website reputation and interactivity could bring a positive attitude towards the reviews. Also, digital marketers must filter and avoid contradictory reviews or reviews that have a bipolar message and reviews expressing numerous emotions to enhance review relevance and consistency.

The current study addresses the need to understand the formation of consumer review attitude through both review and website characteristics using heuristic – systematic model. The paper captures the complex process undergone by the consumer to decipher review attitude and thereby extend the understanding of consumer information processing.

The study aims to determine the role of online retail website experience on brand happiness and willingness to share personal information using the theoretical lens of the Stimulus-Organism-Response (SOR) framework. Further, it explores the role of brand intimacy and brand partner quality in mediating the path between brand happiness and willingness to share personal information.

This study used a cross-sectional survey design to collect data from 439 online retail consumers in India, using an online questionnaire. The data were analysed using Structural Equation Modelling in IBM Amos.

The present study found that online retail website experience is significantly related to brand happiness. The finding also supports that brand happiness was positively and significantly related to ‘consumers' willingness to share personal information. This relationship was fully mediated by brand intimacy. Brand happiness also mediated the relationship between website experience and the willingness to share personal information.

This study contributes to the emerging literature on brand happiness and willingness to share personal information. It establishes a central role of brand happiness as a driver and a mediator of consumers' willingness to share personal information with e-commerce retailers, extending the stimulus-organism-response framework in the context of brand happiness and willingness to share personal information. Further, the study establishes the role of website experience as a marketer (and brand) led driver of brand happiness.

The results have implications for the role of the website in enhancing the consumer experience, which in turn is a driver of brand happiness. Further, managers need to promote brand happiness with the help of website experience to enable consumers’ willingness to share personal information and help organizations customize their marketing campaigns.

This is among the first studies to evaluate brand happiness from the perspective of an online retail website experience and consider consumers’ willingness to share personal information from a branding rather than a technological perspective. Additionally, the study introduces the SOR framework in the context of brand happiness, with website experience acting as a stimulus for consumers, resulting in brand happiness, which is mediated by brand partner quality and brand intimacy (organism), leads to consumers' willingness to share personal information with online retail brands (response).

This paper seeks to demonstrate the value of using social media to capture fashion trends, including the popularity of specific features of clothing, in order to improve the speed and accuracy of supply chain response in the era of fast fashion.

This study examines the role that text mining can play to improve trend recognition in the fashion industry. Researchers used n-gram analysis to design a social media trend detection tool referred to here as the Twitter Trend Tool (3Ts). This tool was applied to a Twitter dataset to identify trends whose validity was then checked against Google Trends.

The results suggest that Twitter data are trend representative and can be used to identify the apparel features that are most in demand in near real time.

The 3Ts introduced in this research contributes to the field of fashion analytics by offering a novel method for employing big data from social media to identify consumer preferences in fashion elements and analyzes consumer preferences to improve demand planning.

The 3Ts improves forecasting models and helps inform marketing campaigns in the apparel retail industry, especially in fast fashion.

The food industry is continuously developing its online services called food delivery applications (FDAs). This study aims to evaluate FDA's importance–performance and identify strategies to maximize its potential gains from a business partner's perspective.

Data are collected from 208 FDA partners in Indonesia. Importance–performance analysis (IPA) is applied to evaluate the FDA feature and extended the theory of potential gain in customer value (PGCV) to achieve potential gains from FDA business partners.

This study provides a clear and measurable direction for future research to develop FDA performance. Owning customer data, revenue sharing and competitive advantage are the most potential gains from joining the FDA from the business partner perspective.

The respondents are restaurants from the micro, small, and medium enterprises levels. Further research should involve middle to upper level restaurants to discover all business partners' perceptions. This will be very helpful for FDA providers interested in improving the best performance for all their partners.

FDA providers must focus on improving and maintaining the features of owning customer data, revenue sharing, competitive advantage, stable terms and conditions, customer interface, building customer loyalty, online presence, user credit rating, promotion and offers, delivery service and sales enhancement to increase consumer satisfaction and meet the expectations desired by business partners.

This research provides a meaningful theoretical foundation for future work. It extends the theory of PGCV using the value of a partner perspective as a substitute for customer value; hence, the authors call it a potential gain in partner value.

This study examines the Robo-Advisors (RA) based on Artificial Intelligence (AI), a new service that digitises and automates investment decisions in the financial and banking industries to provide low-cost and personalised financial advice. The RAs use objective algorithms to select portfolios, reduce behavioural biases, and improve transactions. They are inexpensive, accessible, and transparent platforms. Objective algorithms improve the believability of portfolio selection.

This study adopts a qualitative approach consisting of an exploratory examination of seven different RA case studies and analyses the RA platforms used in the banking industry.

The findings provide two different approaches to running a business that are appropriate for either fully automated or hybrid RAs through the realisation of two platform model frameworks. The research reveals that relying solely on algorithms and not including any services involving human interaction in a company model is inadequate to meet the requirements of customers in decision-making.

This study emphasises key robo-advisory features, such as investor profiling, asset allocation, investment strategies, portfolio rebalancing, and performance evaluation. These features provide managers and practitioners with new information on enhancing client satisfaction, improving services, and adjusting to dynamic market demands.

This study fills the research gap related to the analysis of RA platform models by providing a meticulous analysis of two different types of RAs, namely, fully automated and hybrid, which have not received adequate attention in the literature.

Known for its motto “All Ages, All Races, All Genders,” MAC cosmetics reflects the brand’s commitment to inclusivity and diversity. It has been at the forefront of promoting a wide range of skin tones, ethnicities and gender identities in the beauty industry. If you have ever wondered about the secret behind the success of this mega-brand in the beauty industry, this case study aims to provide a comprehensive overview of the company’s history from its early days to the present and discusses the factors that might build or ruin a brand. Moreover, players in the industry will need to adapt to changing consumer preferences, focus on sustainability and leverage digital technologies to remain relevant and capture market opportunities. Therefore, would it be wise for MAC cosmetics to take forward their expansion plans in India, and what is the motivation behind the expansion? Alternatively, would the implementation of a proactive strategy for product innovation, combined with a stronger presence, result in a more viable and sustainable proposition?

This study relies on online resources for data collection. The data was collected from the secondary sources in the month of November and December 2022. Moreover, the information provided in the case study was validated and supplemented via using different websites, including the company’s website and social media profiles.

The commitment shown and the roles played will have a lasting impact on the society. Additionally, student will be able to learn when and how to seize opportunities, as well as how to manage obstacles that will arise in their entrepreneurial journey. Through this case study, they will be able to learn a variety of business strategies that can be implemented in emerging markets. The conclusion of the study provides the obstacles and hurdles faced by the industry in expanding in the Indian market. The potential solutions were made through providing information for the students, academicians, entrepreneurs and others to compete in the Indian market situation.

This is a real-world case that provides the audience with first-hand experience to comprehend the event and apply their knowledge to form a conclusion and is also effective for decision-making in the dynamic environment of the present day. However, the authors provide a comprehensive overview of the company’s history from its early days to the present and discuss the factors that might build or ruin a brand.

This study aims to identify the major factors influencing the consumers to prefer milk products and also to analyze the awareness level of the Indian consumers.

In this study, the data is obtained through a structured questionnaire from Indian consumers considering convenience sampling under the nonprobability sampling technique. The consumer preference is explained using a multiple-regression model followed by analysis of variance (ANOVA), which shed insight on the significant differences between the variables that influence consumer preference for dairy products.

Investigation is done to analyze the factors influencing the consumers' buying behavior toward milk and its products. The results showed that quality, health consciousness, price and availability are the most influencing factors to buy milk products. Quantity of milk showed a significant relationship between age, monthly income and family size.

This study helps marketing managers to frame the marketing strategies based on consumer preference, quality, health consciousness, price and availability. The research outcome will not only be advantageous for the entrepreneurial perspective but also takes care of consumer likeliness. Though the research reveals the opinion of Indian consumers, it limits the likeliness of the western world. Because of the scarcity of resources, several dairy products are unexplored, which could pave the future scope of research.

The novelty of this study is to identify the quality, health consciousness, price and availability are the most influencing factors to buy milk products considering ANOVA and the multiple regression model.

This study aims to reveal the food preferences and to define the typology of digital nomads as gastro-tourists. In this context, it is aimed to provide information on issues such as the importance of food culture in destination selection for digital nomads, food prices, local dishes, local food markets, religious restrictions, researching cultural cuisines, preparing meals and participating in gastronomic events.

Multiple qualitative methods were utilized in the research for this study. Digital nomads' comments, videos and semi-structured interviews constitute the data sources of the research, and the data obtained were analyzed by content analysis.

As a result of the study, it was seen that digital nomads do consider gastronomy in their destination selection, and five different gastro-tourist profiles were determined. These are Local Flavor Hunters, Gastronomy Adventurers, Budget-Conscious Food Lovers, Cultural Explorers and Health and Sustainability Focused People.

The theoretical contribution of the study is to provide information with regard to the food and beverage preferences of digital nomads and to contribute to filling a gap in the literature. The practical contribution of the study is to help formulate tourism marketing strategies in the destination and strategies for businesses in the tourism industry. Since digital nomads play important roles in destination promotion, it is important to determine their perspectives and typologies towards gastronomy. The results of the study will be useful for tourism industry components, such as academics, experts, travel businesses, tourism destinations and local food producers who research the tourism industry and travel trends.

This paper aims to explore ChatGPT’s (generative pre-trained transformers) potential as a tool for retailers to improve customer experience and boost sales. While it provides benefits like personalized recommendations and 24/7 assistance, there are limitations, like difficulty in understanding unconventional language. The paper stresses careful integration to overcome these limitations and create a better customer experience. Additionally, it discusses the potential for further development and integration of ChatGPT in retail, such as generating product descriptions and virtual try-on experiences. Finally, the paper encourages retailers to embrace ChatGPT to meet their customer needs.

Case-based methodology involves using specific cases or examples to explore a broader issue or phenomenon. Researchers have analysed real-world cases to identify patterns, themes and insights that can be applied to other contexts or situations. This was useful for understanding complex and multifaceted issues as it allowed us to delve deeper into specific examples and explore the nuances of the situation.

While ChatGPT is a powerful tool for retailers, limitations such as difficulty in understanding non-standard accents and unconventional language can arise, causing customer frustration. Retail managers must integrate ChatGPT in a way that enhances customer experience. In the future, ChatGPT has the potential to generate product descriptions, provide virtual try-on experiences and integrate with augmented or virtual reality technology to offer more immersive experiences. Careful consideration and integration can help retailers overcome these limitations and offer personalized recommendations, round-the-clock assistance and an engaging customer experience that improves sales.

The case topic is very much in a novel stage of research and writing.