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Business model (BM) canvases have been used in educational institutions and business incubators for over a decade to assist students and start-up entrepreneurs in developing their business projects. Given the urgency of tackling sustainability challenges, several tools have emerged to stimulate sustainable business modeling (SBM). However, these tools are often too complex for nonexperts in business modeling or sustainability, and thus insufficiently user-friendly for educational contexts. This study aims to address this pedagogical gap by describing the design process of the responsible business model canvas (RBMC).

The authors relied on a design science research methodology involving the active participation of end users, entrepreneurship educators, business coaches and external partners. The authors proposed four criteria and ten subcriteria to analyze existing SBM canvases based on their user-friendliness and to design the initial prototype of the RBMC. The RBMC was subsequently tested in various settings, including classroom assignments and business incubation programs, with over 1,000 university students. The tool was refined and assessed throughout the development process, incorporating feedback from focus groups with start-up entrepreneurs.

Through the development process, the authors created a user-friendly tool to help novice student and start-up entrepreneurs integrate sustainability into their BMs: the RBMC. The canvas consists of 14 building blocks grouped into four areas: consistency (mission, vision, values), desirability (value propositions, customer segments, users and beneficiaries, customer relationships and channels), feasibility (key activities, key resources, key partners and stakeholders and governance) and viability (cost structure, revenues streams, negative impacts and positive impacts).

The research methods and user-friendliness criteria in this study can be applied in other contexts to design tools to support sustainable entrepreneurship education. While the RBMC is currently being used in several educational institutions throughout the world, its impacts in different pedagogical and cultural settings require further validation.

The RBMC is a user-friendly tool to introduce students and start-up entrepreneurs to SBM. It helps raise users’ awareness about sustainability concerns, challenging them to consider issues they might have otherwise overlooked. Some participants even shifted their outlook and were motivated to develop a long-term vision integrating compensatory, mitigative or corrective actions into their BMs.

The RBMC is the outcome of a balanced approach that combines both pragmatic (i.e. user-friendliness) and normative (i.e. sustainability) perspectives. It provides users with a systematic approach for integrating and applying sustainability issues in their business projects.

This study aims to use the voice of the customer (VoC) strategy to collect user-generated content (UGC) compare customer expectations with reality, make the necessary improvements for the business and create personalized strategies for each customer to maximize revenue, focus on hospitality industry in Vietnam market.

This study proposes a synthesis of techniques for a deep understanding of the VoC based on online reviews in the hospitality industry. First, 409,054 comments were collected from websites in the hospitality sector. Second, the data will be organized, stored, cleaned, analyzed and evaluated. Next, research using business intelligence (BI) solutions integrating three models, including net promoter score (NPS), graph model and latent Dirichlet allocation (LDA), based on natural language processing (NLP) technique, experiment on Vietnamese and English data to explore the multidimensional voice of customer’s row. Finally, a dashboard system will be implemented to visualize analysis results and recommendations on marketing strategies to improve product and service quality.

Experimental results allow analysts and managers to “listen to the customer’s voice” accurately and effectively, identify relationships between entities, topics of discussion in favor of positive and negative trends.

The novelty in this study is the integration of three models, including NPS, graph model and LDA. These models are combined based on the BI solution and NLP technique. The study also conducted experiments on both Vietnamese and English languages, which ensures more effective practical application.

Storytelling through virtual reality (VR) combines the strengths of cutting-edge technology with traditional informational campaigns. As a tool for climate change mitigation, VR has been shown to educate individuals and stimulate both emotional and cognitive responses that promote pro-environmental behavior. This paper aims to investigate whether these benefits extend to the field of green investing through an experiment conducted with a sample of small business entrepreneurs.

The experimental design involved making choices between bonds varying in maturity dates, annual interest and environmental classification (regular versus green). To identify potential impacts of the immersive experience on investment decisions, these choices were made both before and after exposure to VR videos illustrating the devastating effects of climate change. A multiple price list was employed to elicit subjects' risk preferences, enabling the joint estimation of the treatment effect and the risk and time preference parameters.

The findings indicate that, when risk and time preference parameters are controlled for, a VR experience can nudge toward green investment choices. This effect is more profound among those who already exhibit a greater propensity to opt for green investments.

Previous research shows that negative emotions, such as guilt, affect pro-environmental intentions, as well as actions, while message vividness through immersive experiences is effective in nudging greener behavior. Since analogous results in the framework of financial investments are not currently available, this paper seeks to test whether VR videos depicting the adverse effects of climate change can generate negative emotions associated with experiencing these effects and make them salient in subsequent investment decisions made by small business entrepreneurs.

This research studies the development of the evolving dynamic system model and explores the important elements or factors and what detailed attributes are the main influences model in achieving the success of a business, industry and management. It also identifies the real and major differences between static and dynamic business management models and the detailed factors that influence them. Later, this research investigates the benefits/advantages and limitations/disadvantages of some research studies. The studies conducted in this research put more emphasis on the capabilities of system dynamics (SD) in modeling and the ability to measure, analyse and capture problems in business, industry, manufacturing etc.

The research presented in this work is a qualitative research based on a literature review. Publicly available research publications and reports have been used to create a research foundation, identify the research gaps and develop new analyses from the comparative studies. As the literature review progressed, the scope of the literature search was further narrowed down to the development of SD models. Often, references to certain selected literature have been examined to find other relevant literature. To do so, a supporting tool (that connects related articles) provided by Google Scholar, Scopus, and particular journals has been used.

The dynamic business and management model is very different from the static business model in complexity, formality, flexibility, capturing, relationships, advantages, innovation model, new goals, updated information, perspective and problem-solving abilities. The initial approach of a static system was applied in the canvas business model, but further developments can be continued with a dynamic system approach.

Based on this study, which shows that businesses are developing more towards digitalisation, wanting the ability to keep up with the era that is moving so fast and the desire to increase profits, an instrument is needed that can help describe the difficulties of the needs and developments of the future world. This instrument, or tool of SD, is also expected to assist in drawing future models and in building a business with complex variables that can be predicted from the beginning.

This study will contribute to the SD study for many business incubator research studies. Many practical in business incubator management to have a benefit how to achieve the business performance management (BPM) in SD review.

The significant differences between static and dynamics to be used for business research and strategic performance management. This comparative study analyses some SD models from many authors worldwide. Their goals behind their strategic business models and encounter for their respective progress.

Firms are increasingly required to make ethical choices when selecting suppliers for their supply chains, and the decisions often rest on individual purchasing managers within the firm. This study builds on the literature on ethical decision-making and the concept of decision frames to investigate the decision-making process of purchasing managers in financially distressed firms. Codes of Conduct (CoC) and how they are enforced (financial rewards and codified procedures for oversight) are studied in terms of their effectiveness in informing and guiding purchasing managers in their supplier selection decisions.

Four sequential experiments were conducted with a total of 648 purchasing managers from manufacturing firms.

The results indicate that purchasing managers in firms facing financial distress are more than four times more likely than purchasing managers in the control groups to select the less ethical supplier in favor of better operational performance. As a potential remedy, it is found that enforcing the firm's CoC help to counteract this tendency and increase ethical supplier selection decisions by 2.1- to 2.6-fold. However, CoC enforcement that invokes multiple conflicting decision frames simultaneously is more likely to impair than promote ethical supplier selection decisions, compared to situations where only one enforcement method is present.

These findings develop an improved understanding of purchasers' decision-making processes and shed light on how to effectively use CoCs to guide these decisions.

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.

The versatility of customer relationship management (CRM) systems has kept these technologies popular over the years. These solutions have been integrated into organizations of all sizes, from large corporations to small- and medium-sized enterprises. Similarly, CRM systems have also found applications in all types of industries and business sectors. All this has been the driving force behind the proliferation of CRM solutions around the world. In this chapter, the author not only reflects on the impact and democratization of CRM systems on business management and marketing strategies but also explores how these technologies can determine the company's income. In particular, the author presents an experiment that analyzes the extent to which the volume of annual investment in CRM solutions can be used to predict annual net income in a sample of companies. Using time series analysis and applying the autoregressive integrated moving average modeling technique, the researcher examines a sample of 10 companies from different industries, and countries, over a 20-year period. The results show the efficiency of the predictive models developed in nine of the 10 companies analyzed. The findings of this study allow us to conclude that there seems to be an association between the investments made in CRM solutions and the income of the companies that invest in these technologies.

Virtual kitchens are a new business phenomenon, and how customers react to the new business model is still a largely unexplored topic. The purpose of this study is to examine the underlying mechanisms of consumers’ different responses to their reasoning of the new and disruptive business model of the virtual kitchen.

Based on the attribution theory and situated focus theory of power, this study conducts three online experiments to test the proposed framework. A total of 487 US residents who had prior experience with restaurant food delivery participated in the studies.

The results indicate that external attribution (vs internal attribution) and ethnic cuisine (vs mainstream cuisine) are more likely to elicit customers’ empathy and justice, leading to higher purchase intentions with virtual kitchens. A mainstream virtual kitchen is better off attributing itself to external factors. The significant effects of causal attribution and cuisine type on purchase intention only exist with powerful customers and those with high moral identity.

The results of this study provide valuable insight to virtual kitchen businesses to better position and market themselves to gain customers’ support. The findings also suggest that ethnic and mainstream restaurants should strategize their marketing communications about virtual kitchens differently.

To the best of the authors’ knowledge, this study is one of the first to provide in-depth insight into the growing phenomenon of virtual kitchens. It also contributes to the extant literature on attribution theory and situated focus theory of power.

With the outbreak of the COVID-19 pandemic, online assessment has become the dominant mode of examination in higher education institutions. However, there are contradictory findings on how students perceive online assessment and its impact on their academic performance. Thus, the purpose of this study is to evaluate the potential impact of online assessment on students' academic performance.

This study proposes a research model based on the task–technology fit theory and empirically validates the model using a survey from students in the UK. In addition, the study conducted four experiments based on paper-based and online assessments and analysed the data using paired sample t test and structural equation modelling.

The findings show that the use of online assessment has a positive impact on students' academic performance. Similarly, the results from the experiment also indicate that students perform better using online assessments than paper-based assessments.

The findings provide crucial evidence needed to shape policy towards institutionalising online assessment. In addition, the findings provide assurance to students, academics, administrators and policymakers that carefully designed online assessments can improve students' academic performance. Moreover, the study also provides important insights for curriculum redesign towards transitioning to online assessment in higher education institutions.

This study advances research by offering a more nuanced understanding of online assessment on students' academic performance since the majority of previous studies have offered contradictory findings. In addition, the study moves beyond existing research by complementing assessment results with the views of students in evaluating the impact of online assessment on their academic performance. Second, the study develops and validates a research model that explains how the fits between technology and assessment tasks influence students' academic performance. Lastly, the study provides evidence to support the wide use of online assessment in higher education.

This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces.

This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framework to gradually adapt AI models’ knowledge while reducing personal data transmission and retention. The framework uses three different federated learning strategies to rapidly learn from different new data sources while minimizing personal data transmission and retention to a central machine.

The findings confirm that the proposed CFL framework can help minimize the use of personal data without compromising the AI model's performance. The gradual learning strategies help develop AI-enabled video surveillance that continuously adapts for long-term deployment in public spaces.

This study makes two specific contributions to advance the development of AI-enabled video surveillance in public spaces. First, it examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Second, it proposes a CFL framework to minimize data transmission and retention for AI-enabled video surveillance. The study provides comprehensive experimental results to evaluate the effectiveness of the proposed framework in the context of facial expression recognition (FER) which involves large-scale datasets.