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Contemporary economies worldwide appreciate the underlying importance of service sectors, culminating in sectorial growth and satisfactory performance of agri-preneurship. This study is premised on two simple explanations that cascaded into two hypothetical questions: (i) Are there inspiring factors for individuals to start up their own agri-preneurship business? (ii) Are there factors hindering individuals from starting up their own agri-preneurship business? These two hypothetical constructs triggered four primary arguments established in the study. This study was conducted in Mbombela local Municipality in category ‘B’, which is made up of the Ehlanzeni District, Mpumalanga South Africa. From a population of 1021 farmers, 458 samples of respondents were obtained and considered to be realistic for the study. The study embraced the mixed research approach, and the field survey was done between the months of February 2022 and June 2022. Descriptive statistics, involving the frequency count and percentages, were used to determine the motives and obstacles of starting up one’s own business. The hypothesis was assessed by employing multinominal logistic regression to determine the relationship between demographic variables and predictor variables. The findings revealed that potential agri-preneurs are affected by an array of inspiring factors in the decision-making process. Further, the results revealed that agri-preneurial spirit is inhibited by numerous challenges. In the shared view of respondents, there was a portmanteau of obstacles impeding the commencement of one’s own business. The results also showed that, in the self-realization variant, age (p < 0.035) and marital status (p < 0.033) were found to be correlated and statistically significant as an inspiration for setting up an agri-preneurship business, while the self-satisfaction variant specified marital status (p < 0.001) to be significant and to negatively (β −1.564) influence inspiration for agri-preneurship. In the independence variant category, two crucial variables, level of formal education (p < 0.005) and farm experience (p < 0.007), were found to be correlated and statistically significant in determining agri-preneurial decisions. This study has several policy propositions: the government and allied stakeholders must consider the motives that inspire potential investors in agriculture. The government must also oversee the reasons for young and old agri-preneurs leaving agri-businesses because of failure. This study underscores the need to subjectively analyze agri-preneurial inspiring factors and obstacles to performance. The contributions of agri-preneurial businesses to economic growth and poverty alleviation justify the need for a concerted effort to motivate potential investors. Another pertinent contribution of the study is highlighting the obstacles of agri-preneurs to enable the government to support and prepare responsive, innovative, and resilient agri-preneurs in South Africa. This paper recommends that starting up one’s own agri-preneurial business is a good idea for abating hunger and unemployment.

The paper examines the nexus between working capital management (WCM) and financial performance of listed non-financial firms in Ghana. An unbalanced panel data for the period 2008 to 2021 was used for the study. It is observed that the residual terms of the models were cross-sectionally independent and all the series were first-differenced stationary and cointegrated in the long term The elasticities of the predictors were explored via the Fully Modified Ordinary Least Squares (FMOLS) and the Dynamic Ordinary Least Squares (DOLS) techniques. The findings of the study indicate that WCM proxied by accounts receivable period (ACP), accounts payment period (APP), and inventory turnover period (ITP) have significant positive effect on firms’ financial performance measured by return on assets (ROA), return on equity (ROE), and return on capital employed (ROCE). This suggests that the working capital management practices of non-financial firms in Ghana improve their financial performance. Also, firm size and asset growth improve firm financial performance. On the causalities between the variables, bidirectional causalities between ACP, APP, ITP, size, and thecompanies’ ROA, ROE, and ROCE are disclosed. Finally, causality from growth to the ROA, ROE, and ROCE of the firms are unraveled. It is recommended that policy makers of non-financial firms in Ghana should not overlook WCM practices in their financial decisions, since ignoring them could seriously compromise the firms’ short- and long-term sustainability.

Using Chinese A-share listed companies from 2011 to 2020 as a research sample, this paper examines the relationship between negative media coverage and corporate ESG performance using a two-way fixed-effects model. It is found that, first, negative media coverage can effectively promote corporate ESG performance. Second, the mediation mechanism study shows that negative media coverage positively promotes corporate ESG performance by increasing the degree of corporate financing constraints and information asymmetry and prompting corporations to change their ESG governance level. Third, the results of the heterogeneity test find that the positive relationship between negative media coverage and corporate ESG performance is more pronounced among firms without executives with overseas backgrounds, and the positive relationship between the two is more significant after the promulgation of China’s Code of Governance for Listed Companies in 2018. Fourth, further discussion revealed that negative media coverage has the strongest promotion effect on the performance of corporate environmental governance, followed by social governance performance, and lastly, corporate governance performance. The research in this paper contributes to an in-depth understanding of the impact of negative media coverage on corporate ESG performance and provides empirical evidence to facilitate policy formulation related to the role of media monitoring and to fully utilize the media’s role in corporate ESG governance.

Since 2012, researchers have explored various factors influencing Bitcoin prices. Up until the end of July 2023, more than 9100 research papers on cryptocurrencies were published and indexed in the Web of Science Clarivate platform. The objective of this paper is to analyze the impact of publications on Bitcoin prices. This study aims to uncover significant themes within these research articles, focusing on cryptocurrencies in general and Bitcoin specifically. The research employs latent Dirichlet allocation to identify key topics from the unstructured abstracts. To determine the optimal number of topics, perplexity and topic coherence metrics are calculated. Additionally, the abstracts are processed using BERT-transformers and Word2Vec and their potential to predict Bitcoin prices is assessed. Based on the results, while the research helps in understanding cryptocurrencies, the potential of academic publications to influence Bitcoin prices is not significant, demonstrating a weak connection. In other words, the movements of Bitcoin prices are not influenced by the scientific writing in this specific field. The primary topics emerging from the analysis are the blockchain, market dynamics, transactions, pricing trends, network security, and the mining process. These findings suggest that future research should pay closer attention to issues like the energy demands and environmental impacts of mining, anti-money laundering measures, and behavioral aspects related to cryptocurrencies.

The emergence of the crowdsourcing platform enables seekers to obtain higher-quality services at lower costs. High-quality services are often provided by high-quality solvers, which is the key to the sustainable development of crowdsourcing platforms. Therefore, how to attract more high-quality solvers to participate needs to be focused on. Most previous studies that used stock data to measure crowdsourcing performance failed to describe the contest process of high-quality solvers’ behavior. Different from the previous study, this paper explores the information signals that influence the participation of high-quality solvers in the dynamic process of crowdsourcing contests. Based on the creative projects of the Winvk platform, dynamic models affecting the participation of high-quality solvers are constructed from the perspective of reducing information asymmetry, and the effects of quality signals and intention signals are explored in depth. The results show that for logo design projects, clear information display and monetary mechanisms have a significant impact on alleviating information asymmetry and attracting the participation of high-quality solvers. Interestingly, the effect of market competition on high-quality solvers shows a U-shaped change. The research results provide a reference for enterprises to reduce information asymmetry, obtain high-quality solutions, and enrich the theoretical application in the field of crowdsourcing.

The latest effort in delivering computing resources as a service to managers and consumers represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to users over the internet from large-scale data centers. However, with the advent of the cloud-based IoT and artificial intelligence (AI), which are advancing customer experience automations in many application areas, such as recommender systems (RS), a need has arisen for various modifications to support the IoT devices that are at the center of the automation world, including recent language models like ChatGPT and Bard and technologies like nanotechnology. This paper introduces the marketing community to a recent computing development: IoT-driven fog computing (FC). Although numerous research studies have been published on FC “smart” applications, none hitherto have been conducted on fog-based smart marketing domains such as recommender systems. FC is considered a novel computational system, which can mitigate latency and improve bandwidth utilization for autonomous consumer behavior applications requiring real-time data-driven decision making. This paper provides a conceptual framework for studying the effects of fog computing on consumer behavior, with the goal of stimulating future research by using, as an example, the intersection of FC and RS. Indeed, our conceptualization of the “fog-based recommender systems” opens many novel and challenging avenues for academic research, some of which are highlighted in the later part of this paper.

This paper examines how businesses in Taiwan’s servicescapes are adapting to the growing trend of mobile payments and innovation ecosystems. Through the analysis of four case studies, we uncover the strategies these firms employ to address the challenges posed by changing consumer payment habits. Our research reveals that these companies are establishing efficient mechanisms within their ecosystems, supported by well-structured organizational frameworks. By leveraging innovation ecosystems, they are reshaping financial services and promoting collaborative growth among participants through technology, platforms, resource sharing, and knowledge exchange. This collaborative approach is driving significant changes in the sector, helping these businesses navigate through various challenges while fostering innovation and growth. Additionally, the scarcity of comprehensive observations of the digital payment ecosystem highlights the necessity for further exploration of actor interactions, regulatory mechanisms, and ecosystem management strategies. Such research efforts are crucial for enhancing our understanding of the evolving landscape of digital payments and innovation ecosystems, facilitating informed decision-making and promoting sustainable development in this dynamic industry.

Artificial intelligence (AI) agents are widely used in the retail and distribution industry. The primary objective was to investigate whether the gender of AI agents influences trust and grounding. This paper examined the influence of AI agent gender and brand concepts on trust and grounding within virtual brand spaces. For this purpose, it used two independent variables: brand concept (functional vs. experiential) and AI agent gender (male vs. female). The dependent variables included AI agent trust and grounding. The study revealed that in virtual brand spaces centered around a functional concept, male AI agents generated higher levels of trust than female AI agents, whereas, when focused on an experiential concept, female AI agents induced higher levels of grounding than male AI agents. Furthermore, the findings indicate that the association between customers’ identification with AI agents and recommendations for actual brand purchases is mediated by trust and grounding. These findings support the idea that users who strongly identify with AI agents are more inclined to recommend brand products. By presenting alternatives that foster the establishment and sustenance of a meaningful, sustainable relationship between humans and AI, this study contributes to research on human–computer interactions.

Meeting customer requirements in software project management, even for large digital enterprises, proves challenging due to unpredictable human factors. It involves meticulous planning and environmental factor analysis, ultimately benefiting both companies and customers. This paper came as a natural extension of our previous work where we left ourselves curious about what impact environmental complexity factors (ECFs) have in a use case point (UCP) approach. Additionally, we wanted to possibly decrease the mean magnitude relative error (MMRE) with deep learning models such as long-short-term-memory (LSTM) and gradient recurrent unit (GRU). The data augmentation technique was used to artificially increase the number of projects, since in the industry world, digital enterprises are not keen to share their data. The LSTM model outperformed the GRU and XGBoost models, while the average MMRE in all phases of the experiment for all models achieved 4.8%. Moreover, the post-agnostic models showed the overall and individual impact of eight ECFs, where the third ECF “team experience” on a new project has been shown as the most influential one. Finally, it is important to emphasize that effectively managing human factors within ECFs in UCPs can have a significant impact on the successful completion of a project.

Modern e-businesses heavily rely on advanced data analytics for product recommendations. However, there are still untapped opportunities to enhance user interfaces. Currently, online stores offer a single-page version to all customers, overlooking individual characteristics. This paper aims to identify the essential components and present a framework for enabling multiple e-commerce user interfaces. It also seeks to address challenges associated with personalized e-commerce user interfaces. The methodology includes detailing the framework for serving diverse e-commerce user interfaces and presenting pilot implementation results. Key components, particularly the role of algorithms in personalizing the user experience, are outlined. The results demonstrate promising outcomes for the implementation of the pilot solution, which caters to various e-commerce user interfaces. User characteristics support multivariant websites, with algorithms facilitating continuous learning. Newly proposed metrics effectively measure changes in user behavior resulting from different interface deployments. This paper underscores the central role of personalized e-commerce user interfaces in optimizing online store efficiency. The framework, supported by machine learning algorithms, showcases the feasibility and benefits of different page versions. The identified components, challenges, and proposed metrics contribute to a comprehensive solution and set the stage for further development of personalized e-commerce interfaces.

Social media platforms have allowed many people to publicly express and disseminate their opinions. A topic of considerable interest among researchers is the impact of social media on predicting the stock market. Positive or negative feedback about a company or service can potentially impact its stock price. Nevertheless, the prediction of stock market movement using sentiment analysis (SA) encounters hurdles stemming from the imprecisions observed in SA techniques demonstrated in prior studies, which overlook the uncertainty inherent in the data and consequently directly undermine the credibility of stock market indicators. In this paper, we proposed a novel model to enhance the prediction of stock market movements using SA by improving the process of SA using neutrosophic logic (NL), which accurately classifies tweets by handling uncertain and indeterminate data. For the prediction model, we use the result of sentiment analysis and historical stock market data as input for a deep learning algorithm called long short-term memory (LSTM) to predict the stock movement after a specific number of days. The results of this study demonstrated a predictive accuracy that surpasses the accuracy rate of previous studies in predicting stock price fluctuations when using the same dataset.

Studying customer behavior and anticipating future trends is a challenging task, as customer behavior is complex and constantly evolving. To effectively anticipate future trends, businesses need to analyze large amounts of data, use sophisticated analytical techniques, and stay up-to-date with the latest research and industry trends. In this paper, we propose a comprehensive framework to identify trends in consumer behavior using multiple layers of processing, including clustering, classification, and association rule learning. The aim is to help a major retailer in Saudi Arabia better understand customer behavior by utilizing the power of big data analysis. The proposed framework is presented as being generalized to gain insight into the generated big data and enable data-driven decision-making in other relevant domains. We developed this framework in collaboration with a large supermarket chain in Saudi Arabia, which provided us with over 1,000,000 sales transaction records belonging to around 30,000 of their loyal customers. In this study, we apply our proposed framework to those data as a case study and present our initial results of consumer clustering and association rules for each cluster. Moreover, we analyze our findings to figure out how we can further utilize intelligence to predict customer behavior in clustered groups.

The Internet of Things (IoT) was born from the fusion of virtual and physical space and became the initiator of many scientific fields. Economic sustainability is the key to further development and progress. To keep up with the changes, it is necessary to adapt economic models and concepts to meet the requirements of future smart environments. Today, the need for electronic commerce (e-commerce) has become an economic priority during the transition between Industry 4.0 and Industry 5.0. Unlike mass production in Industry 4.0, customized production in Industry 5.0 should gain additional benefits in vertical management and decision-making concepts. The authors’ research is focused on e-commerce in a three-layer vertical IoT environment. The vertical IoT concept is composed of edge, fog, and cloud layers. Given the ubiquity of artificial intelligence in data processing, economic analysis, and predictions, this paper presents a few state-of-the-art machine learning (ML) algorithms facilitating the transition from a flat to a vertical e-commerce concept. The authors also propose hands-on ML algorithms for a few e-commerce types: consumer–consumer and consumer–company–consumer relationships. These algorithms are mainly composed of convolutional neural networks (CNNs), natural language understanding (NLU), sequential pattern mining (SPM), reinforcement learning (RL for agent training), algorithms for clicking on the item prediction, consumer behavior learning, etc. All presented concepts, algorithms, and models are described in detail.

The digital economy and the sharing economy have changed the role citizens may acquire in society. Citizens can perform at least two roles from the open government perspective: on the one hand, they can be passive users/demanders of information and, on the other hand, they can provide or produce the information in an active manner. The objective of this paper is to offer a theoretical model to explain citizens’ incentives to participate in open government projects. Which is the opportunity cost of participation for the citizen? Which are the drivers of the preferences for the social good? This model is based on the utility function and consumption theory. We complement the theoretical framework with an exploratory–descriptive analysis based on a case study’s primary data about citizen participation. In democracy projects where citizens actively collaborate and could earn monetary gains or become entrepreneurs, the opportunity cost of participation is lower than in a passive type and the amount of the social good depends on the preferences. Preferences for social goods are related to community experiences and e-government and they also affect the decision to participate. Very few studies in the field of open government have pretended to explain citizens’ participation by using microeconomic foundations.

In this paper, we explore the importance of accounting for climate when determining the impact of weather on product sales. Using a France-wide scanner panel dataset provided by our industry partner, we show that if climate is not accounted for, product categories may be misclassified as being weather sensitive when they are not, and vice versa. This is motivated by previous research and industry reports that suggest a relationship between weather and retail sales. However, these studies often fail to distinguish between weather and climate, leading to inaccurate conclusions. Our results highlight the need to control for climate in order to accurately assess the effects of weather on retail sales. We use ordinary least squares regression to estimate the relationship between temperature and sales for 29 different product categories. The regression models control for various factors, including shelf space allocation, week of observation, quantity purchased, promotion, store brand, store surface area, store competition, and consumer behavior measures. We find that when accounting for climate, only a subset of the product categories is sensitive to weather. Additionally, we show that climate can be approximated using a week index, eliminating the need for additional data collection and approximation efforts. Our findings have implications for both researchers and practitioners. Researchers should be aware of the importance of accounting for climate when studying the impact of weather on retail sales, as failing to do so may lead to erroneous conclusions. Practitioners can use our results to inform their marketing and sales strategies, taking into account the weather sensitivity of different product categories and the role of climate in shaping consumer behavior. Overall, our study emphasizes the need to consider climate when determining the impact of weather on retail sales, and provides practical insights for retailers and economists.

Children’s access and opportunities to play sports are influenced by categories of difference, such as gender, race, and socioeconomic status. In order to provide an inclusive community and facilitate the recruitment and retention of diverse youth, athletic organizations should be aware of implicit bias and how this can affect the relationship between volunteers and the children they serve. This paper presents a formative process evaluation of a diversity, equity, and inclusion (DEI) training program for a non-profit athletic organization. Training was implemented in person in a group setting with multiple opportunities for group discussion. Mixed methods were used to monitor (1) the implementation of training, (2) its effects on attitudes throughout training, and (3) impact one month after training. Findings demonstrate that the program increased participants’ ability to identify DEI initiatives and sense of belonging to the organization, but these effects had declined by the end of the first month after training. However, participants’ self-beliefs regarding DEI and overall willingness to engage in DEI activities remained enhanced one month after training. Qualitative data were helpful in providing insight into how training impacted participants and their interactions within and outside of the organization. Through this mixed methods approach, we can conclude that DEI training did in fact have a positive impact on the organization, but further evaluation and training may be necessary to address the decline in some effects seen one month after training.

This paper presents an empirical analysis of e-commerce data obtained through Google Analytics (GA) from two small businesses’ perspectives: an IT components company and a tourism agency website located within the same county in Romania. The objective of our study is to examine the enduring effects of the COVID-19 pandemic and seasonal variations over the last four years. The data collection spanned from January 2019, predating the onset of the COVID-19 pandemic, until mid-February 2023. To facilitate our analysis, we categorize the GA metrics into groups that encompassed website performance, site accessibility, and user behavior for the IT company. As for the tourism agency, we focus on website accessibility, user behavior, and marketing campaigns. Our goal is to empirically group or associate GA metrics according to their intrinsic meaning and check if each group reflects a certain latent concept (such as user behavior or site accessibility). Furthermore, our study aims to formulate and test five hypotheses regarding the immediate and long-lasting impact of the COVID-19 pandemic on the operations of small businesses. Our contribution consists of formulating and verifying the five hypotheses by providing descriptive data from the results of the Pearson correlation test, empirically grouping the GA metrics and verifying whether they reflect certain latent factors or topics, interpreting the results from the application of the ANOVA technique and Scarpello’s adaptation of the one factor test, respectively.

It is crucial to explore the impact of digital financial inclusion on tourism for national economic development. This paper utilizes panel data from 256 prefecture-level cities in China between 2011 and 2019 to examine the influence of digital financial inclusion on tourism. The findings demonstrate that digital financial inclusion significantly contributes to the development of the tourism industry. Notably, its coverage breadth, depth of use, and level of digitalization also have positive effects. Mechanism analysis reveals that digital financial inclusion facilitates the growth of tourism by supporting the development of tourism enterprises and enhancing consumer spending. Heterogeneity analysis further reveals regional and urban disparities in the promotion of digital financial inclusion, with the effect being more pronounced in the eastern region and larger cities. In comparison to existing studies, this paper delves into the mechanisms through which digital financial inclusion impacts tourism, as well as investigates regional and city size discrepancies. Consequently, governments should strive to foster the development of digital financial inclusion to attract market players and promote the advancement of residents’ consumption, thereby bolstering tourism development.

This paper develops a duopoly model for user-generated content (UGC) platforms, which compete for consumers and content producers in two-sided markets characterized by network externalities. Each platform has the option to invest in a content quality assurance (CQA) system and determine the level of advertising. Our model reveals that network effects are pivotal in shaping the platforms’ optimal strategies and user behavior, specifically in terms of single vs. multi-homing. We find that when network effects for producers are weak, consumers tend to engage in multi-homing while producers prefer single-homing. Conversely, strong network effects lead to the opposite behavior. Furthermore, our model demonstrates that user behavior and network effects dictate whether a platform is incentivized to incorporate advertisements and/or invest in CQA. Generally, weak network effects prompt a platform to invest in a CQA system, unless both consumers and producers engage in multi-homing. Our model’s results highlight the importance for platform companies to evaluate the extent of network effects on their platform in order to anticipate user behavior, which subsequently informs the optimal CQA and advertising strategy.

Cutting-edge technologies, with a special emphasis on the Internet of Things (IoT), tend to operate as game changers, generating enormous alterations in both traditional and modern enterprises. Understanding multiple uses of IoT has become vital for effective financial management, given the ever-changing nature of organizations and the technological disruptions that come with this paradigm change. IoT has proven to be a powerful tool for improving operational efficiency, decision-making processes, overall productivity, and data management. As a result of the continuously expanding data volume, there is an increasing demand for a robust IT system capable of adeptly handling all enterprise processes. Consequently, businesses must develop suitable IoT architectures that can efficiently address these continually evolving requirements. This research adopts an incremental explanatory approach, guided by the principles of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). A rigorous examination of 84 research papers has allowed us to delve deeply into the current landscape of IoT research. This research aims to provide a complete and cohesive overview of the existing body of knowledge on IoT. This is accomplished by combining a rigorous empirical approach to categorization with ideas from specialized literature in the IoT sector. This study actively contributes to the ongoing conversation around IoT by recognizing and critically examining current difficulties. This, consequently, opens new research possibilities and promotes future developments in this ever-changing sector.

The growth of e-commerce has led to the widespread use of DeepCTR technology. Among the various types, the deep interest network (DIN), deep interest evolution network (DIEN), and deep session interest network (DSIN) developed by Alibaba have achieved good results in practice. However, the above models’ use of filtering for the user’s own historical behavior sequences and the insufficient use of context features lead to reduced recommendation effectiveness. To address these issues, this paper proposes a novel article model: the deep filter context network (DFCN). This improves the efficiency of the attention mechanism by adding a filter to filter out data in the user’s historical behavior sequence that differs greatly from the target advertisement. The DFCN pays attention to the context features through two local activation units. This model greatly improves the expressiveness of the model, offering strong environment-related attributes and the adaptive capability of the model, with a significant improvement of up to 0.0652 in the AUC metric when compared with our previously proposed DICN under different datasets.

For more than two decades, developed countries have been confronted with two trends that have implications for the emergence of engaging senior citizens in the digital environment. On the one hand, there is an increasing proportion of senior citizens in the total population. On the other hand, the application of ICT in all areas of life and business is accelerating. This paper investigates the relationship between self-efficacy, social support, and social influence on Internet usage among senior citizens in Croatia. Survey research was conducted on a sample of Croatian senior citizens, and a structural equation mode was developed for testing the research hypothesis. Self-efficacy influenced both the Intensity and obstacles of Internet usage in a positive and negative manner, respectively. Social influence directly decreased the obstacles to Internet usage, while the relationship with the Intensity of the Internet was indirect through self-efficacy. Social support had only an indirect association with Intensity of Internet usage. Results have relevant implications for programmes aiming to enhance Internet usage among senior citizens, which should focus on the educational programmes fostering perceived self-efficacy of Internet usage among senior citizens.

Digital start-ups play a crucial role in boosting the economies of many countries through technological innovations. Several studies have been conducted assessing digital start-ups or digital entrepreneurship, mainly from the perspective of the Global North. However, gaps exist in the literature regarding digital ecosystems, especially in the context of developing countries (the Global South), such as South Africa. This study fills this gap by exploring the structure as well as highlighting the hindering factors of the start-up ecosystem in South Africa. In addition, the study explores the influential factors of the digital start-up ecosystem and models that can be used to assess upscaling for the growth of new digital start-up ventures. The study conducted a systematic literature review using the PRISMA framework. The Scopus-indexed database was used to source published peer-reviewed papers on digital ecosystems between 2017 and 2023. Key findings of the study pertaining to South Africa’s start-up ecosystem revealed that the country is producing thriving digital start-ups. The current study also identified several challenges that affect the development of digital start-ups in South Africa. Some of the challenges include regulatory barriers, skills shortages, a lack of funding, and a digital infrastructure gap, among others. Furthermore, work is being conducted by ecosystem stakeholders to address these challenges, with a greater collective and cohesive effort needed to effectively address the hindering factors. The study advocates for intervention as well as policy and practitioner implications that could be utilised by ecosystem stakeholders, particularly entrepreneurs in the digital market. The research findings pertain to the South African start-up ecosystem but have greater appeal and relevancy for many developing start-up ecosystems globally, especially in the Global South.

Condominiums and similar properties use a stratum to manage daily operations, and owners fund it through strata fees. While existing strata fee management systems may be able to handle such funds, such systems could be more inherently transparent. It is possible to leverage the digital ledger from blockchain networks and smart contracts to build a fully transparent strata fee management system. This paper proposes designing a strata fee management system based on a smart contract in the Ethereum network. Both strata corporations and homeowners can interact with the smart contract to execute common procedures such as paying strata fees and handling expenses. Using smart contracts for strata fee management, it is believed that the chance of fraud by strata corporations is lowered compared to other systems.

Conversational robots powered by artificial intelligence (AI) are intensively implemented in the insurance industry. This paper aims to determine the current level of acceptance among consumers regarding the use of conversational robots for interacting with insurers and seeks to identify the factors that influence individuals’ behavioral intention to engage with chatbots. To explain behavioral intention, we tested a structural equation model based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. It was supposed that behavioral intention is influenced by performance expectancy, effort expectancy, social influence, and trust, and by the moderating effect of insurance literacy on performance expectancy and effort expectancy. The study reveals a significant overall rejection of robotic technology among respondents. The technology acceptance model tested demonstrates a strong ability to fit the data, explaining nearly 70% of the variance in behavioral intention. Social influence emerges as the most influential variable in explaining the intention to use conversational robots. Furthermore, effort expectancy and trust significantly impact behavioral intention in a positive manner. For chatbots to gain acceptance as a technology, it is crucial to enhance their usability, establish trust, and increase social acceptance among users.

There has been increasing attention given to the relationship between children’s rights and business due to a greater understanding of the direct and indirect impacts that businesses have on children and youth. Concomitantly, many businesses are involved in charitable work. Do charitable activities performed by businesses provide an entry point for considering children’s rights? Further, do these charitable activities facilitate an opportunity for the amalgamation of corporate social responsibility (CSR) and business and human rights? It is hypothesised that charitable contributions can facilitate a greater understanding of children’s rights and subsequently advance implementation. Accordingly, businesses can recognise their capacity to do more than mitigate their negative impacts, and positively influence the realisation of children’s rights. This can be facilitated through the improved assessment of charitable contributions using a child-rights-based approach. This research paper is informed by qualitative individual interviews with 15 stakeholders from pertinent professional sectors, five focus groups with 38 children and youth, and academic and grey literature reviews. It is concluded that attention to impact assessment offers a valuable avenue forward by which to knit the threads of activity regarding both human rights and corporate social responsibility through accountability. A proposed checklist may stimulate future actions and developments in children’s rights within and outside of businesses.

Although there has been a worldwide cry for gender equity within organizations, gender discriminatory practices continue to be a challenge. Many women still suffer from gender discrimination and remain at the bottom of organizational structures despite their efforts to ascend. This paper seeks to examine the link between gender discriminatory practices and women’s skill development and progression within the workplace. The study espoused a quantitative approach. A questionnaire survey was self-administered online to 412 women through a convenient non-probability sampling method. Descriptive tendencies, test normality, validity, reliability, and regression analysis were performed using the statistical package for the social sciences (SPSS), AMOS 27. The results reveal that women’s skill development is impacted by workplace gender discriminatory practices, and the career progression of women is linked to their skill development. Yet it rejects the claim that workplace gender discriminatory practices impact women’s career progression. While having a gender-friendly work environment is applaudable, developing women’s skills and promoting their advancement at work will require more effort from companies. Organizations need to be deliberate about the skills development and career progression of women and institutionalize initiatives that directly encourage women to engage in developmental activities as well as initiatives geared towards promoting women’s career advancement.

Decision makers’ behavioral preferences have always been important in coordinating the supply chain. Decision makers need to choose a partner wisely to increase the profitability of the entire supply chain, especially in the competitive e-commerce environment. In this paper, we examine a two-echelon e-commerce supply chain with one retailer and one supplier using the most popular wholesale price contract to facilitate collaboration. Traditional research has shown that the classical expectation model cannot coordinate the supply chain. We apply the focus theory of choice to describe the retailer’s behavior as a follower, and we examine the impact of the retailer’s pricing decisions on the supplier under different focus preferences and the coordination for the entire supply chain. The lower the parameter φ, which represents the degree of positivity, and the higher the parameter κ, which represents the level of confidence, the closer the profit of the whole supply chain is to the coordination result—both are visualized through numerical experiments and images. In the case of φ determination, the lower the κ, the better the supply chain coordination. The finding implies that the retailer may be able to coordinate the supply chain and produce better results than the expectation model when he or she makes choices using a positive evaluation system that includes both higher levels of optimism and lower levels of confidence. The findings of the FTC model can simultaneously offer a theoretical foundation for expanding collaboration among supply chain participants and management insights for decision makers to choose cooperation partners.

Live-streaming e-commerce is the future development direction of the retail industry. When retailers choose a live-streaming e-commerce platform, they face the test of various risks of the platform, such as insecure control of capital flow, insufficient support of public domain traffic, etc. Therefore, it is necessary to evaluate the risks of the platform to help retailers identify the platform with the lowest risk. Considering the complexity of the risks of live-streaming e-commerce platforms and the ambiguity of the decision-makers thinking, the current method for multi-criteria group decision-making (MCGDM) method in a fuzzy environment rarely discusses the decision-makers weight for the criterion. This paper proposes interval-valued intuitionistic fuzzy multi-criteria group decision-making based on the decision-makers’ professionalism to evaluate the platform’s risks. This method determines the decision-maker’s weight for the criterion based on the professionalism of the decision-maker and uses the technique for order preference by similarity to an ideal solution (TOPSIS) method to rank the alternative platforms. Finally, a risk assessment of the agricultural product live-streaming e-commerce platforms is used as a case study to demonstrate the feasibility and effectiveness of the proposed method. This research will not only provide practical guidance for retailers to choose the live-streaming e-commerce platform with the lowest comprehensive risk but also provide ideas for the research of live-streaming e-commerce from the perspective of risk assessment.

This paper examines the effect of CSR perceptions on online recruitment practices and outcomes in the Vietnamese food and beverage sector. Semi-structured interviews were conducted with seventeen participants, including CEOs, HR (human resource) staff and store managers. The results show that CSR perception varies and is exemplified through employee-related activities, ethical products, environment, philanthropy, and international standards. The study is novel in establishing that, despite high unemployment in developing economies, CSR communication in online recruitment sites impacts job applicants’ choice of company. While the strategic potency of CSR has been established, this study brings to light its critical significance for human resources processes, particularly online recruitment, in an emerging economy context. The study has considerable implications for practitioners and researchers, suggesting the necessity to strategically manage the interface between CSR and online recruitment.

The deployment of Blockchain technology in the tourism industry is already becoming a reality with the gradual emergence of innovative business models. At its core is the promise of improving the efficiency of the tourism service value chain and enhancing the quality of the service provided to the end customer. This paper analyses research trends focused on using Blockchain technology in tourism. The aim is to determine how this technology impacts the tourism sector and its sustainability. A systematic review, descriptive bibliometric analysis, and network analysis based on co-authorship, co-citation, and keyword analysis criteria, among others, have been used. The results reveal that the subject matter analysed is generating a growing trend in academic research in the fields of sustainable management and supply chain efficiency. The activities in the tourism sector that are incorporating this technology to a greater extent are those related to the areas of marketing, logistics, and smart business models, according to the data extracted from the analysis. This technology already enables the application of solutions that predict and promote tourist behaviour based on sustainable behaviour and consumption habits, generating value for the different stakeholders.

Observing the fast development of livestreaming, this paper investigates its adoption on the retail platform and examines its impact on merchants. We develop a game-theoretic model in which a leading retailer and a third-party seller engage in price competition. Our model fully considers the initiative of live streamers in this asymmetric competition. We find that the streamer’s cost and the seller’s initial awareness are two key factors affecting the adoption of livestreaming. Specifically, when the streamer’s cost is low, or it is intermediate and the seller’s initial awareness is high, the retailer adopts and opens livestreaming and the seller also adopts it; when both factors are intermediate, the retailer adopts livestreaming but does not open it to the seller; when both factors are high, the retailer adopts and opens livestreaming but the seller does not adopt it; otherwise, the retailer does not adopt livestreaming. Our results also suggest that the presence of livestreaming benefits the retailer but may hurt the seller especially when the seller’s initial awareness is high. Our findings provide relevant and useful implications for both the platform retailer and third-party seller in their livestreaming decisions.

Understanding how competitors act in a market is a critical component of strategic decision-making. In this paper, we propose a method to extract firm events from the textual content generated by firms in the market and explore the competitive relationships among firms based on the spatiotemporal homogeneity of events of different firms. To this end, we first introduce experts to define a series of business events based on the content of corporate-generated texts; then, we propose algorithms to extract and enrich the feature words (triggers) of these business events to form better event classifiers. We subsequently use these classifiers to identify the business events recorded in all online texts published by companies. Finally, based on these results, we can obtain a sequence of activities/events for each firm in the market, which can be used to identify the evolutionary patterns of firms’ behavior in the market, as well as their potential competitive relationships. Considering that competition between companies in the market appears to be continuous at the strategic level, but the implementation of competitive behavior is expressed through their “events” in the market, identifying whether companies are “competing” in the market requires timely observation of the information about “events” in the market. However, obtaining accurate market information is complex and costly. Therefore, this study provides a way to bridge the gap between social media data and market competition “events”.

E-commerce is an area in which success largely depends on the e-commerce applications used. Currently, there are many studies that focus on various technological innovations in e-commerce systems, but a comprehensive concept of their improvement is lacking. In this regard, the main objective of the article was to explore the technological possibilities for the improvement of e-commerce systems and to propose a conceptual framework for their maintenance and development. The chosen research approach included a combined methodology–text mining analysis of existing developments and identifying the key themes and trends in papers and deep scientific analyses of the most-cited developments. As a result, a conceptual framework for the maintenance and development of e-commerce applications was proposed. To test the approach, publications from the scientific literature database Scopus, on the topic “e-commerce applications” were used. Experiments and text processing were performed with the software RapidMiner Studio. Several main directions for the development of e-commerce systems have been defined: the application of modern development tools; the technological base; and the possibilities of integration. Future research may focus on detailing each direction.

The role of blockchain in new business model development requires greater focus because the technology is still in its infancy. Thus, there has been little research on the effects of the various blockchain networks (such as public, private, and consortium). This finding prompted a thorough investigation of new blockchain-based business models created between 2012 and 2022 to close this gap. This review’s focus is on journals, and duplicate articles have been removed. Works based on interviews, articles in press, non-English articles, reviews, conferences, book chapters, dissertations, and monographs are also not included. Seventy-five papers from the past ten years are included in this evaluation. This study examines the current state of new blockchain-based business models. Additionally, the implications and applications in the related literature have been investigated. These findings highlight numerous open research questions and promising new directions for investigation, which will likely be helpful to academics and professionals. The business strategies built on blockchain are currently on a path with a rapid upward trajectory. Blockchain technology offers businesses numerous chances to modify and develop new company models. By changing the conventional framework, blockchain innovation leads to the development of new methods for developing company models. The supportive potential of blockchain technologies such as NFT and P2E is increasingly being coupled with the development of new corporate projects and the modification of current business models. Since this field of study is still fairly new, researchers will have fresh opportunities to analyze its characteristics.

The purpose of this paper is to assess the impact of the COVID-19 pandemic on rural e-customers’ preferences in terms of last mile delivery and products purchased via the Internet. This paper is based on the theory of last mile delivery (with a strong focus on the methods of delivery) and customers’ preferences. It combines both theories to fulfil the paper’s goal. In the paper, the literature review and multivariate data analysis of rural e-customers in Poland were used. The hypothesis states that there is a requirement for different last mile delivery solutions in rural areas than in urban areas in Poland. The research was carried out on a sample of 1071 adult Poles, who are residents of rural areas, using the standardized and structured computer-assisted interview (CAWI). The authors used the chi-square independence test, the Student t-test, one-way analysis of variance (ANOVA), regression models, and logistic models. The research shows that more than 60% of respondents living in rural areas order goods purchased online with a home delivery method and that online shopping preferences’ changes are not so significant after the occurrence of COVID-19. Both before and after the pandemic, rural e-customers made online purchases most often 2–3 times a month. Nevertheless, some differences in the preferences of rural e-customers in terms of last mile delivery and purchasing preference can be seen considering their age, gender, or family size. The survey found that smaller families were more likely to choose free home delivery with a free return after the pandemic. The research presented in this paper provides essential information for courier companies, stores, and online platforms alike. Knowledge of preferences and their changes after the occurrence of COVID-19 can support the reorganization of last mile delivery to meet the needs of e-customers and to adapt to the increasing demands for sustainable transportation.

Over recent years, social commerce has evolved into a powerful segment of e-commerce, creating new opportunities for brands of all types and sizes. However, if social commerce is to continue to grow and deliver the many benefits it promises, it must address a number of key challenges, including privacy, trust, and ethical concerns. This paper explores the extent to which privacy issues affect the attitudes and behaviours of social media platform (SMP) users towards social commerce, and investigates whether these attitudes and behaviours are a function of cultural context. The approach adopted for the research is a two-stage method, which initially uses semi-structured interviews of social-commerce users to identify their key privacy concerns. These concerns are then used to develop, using the theory of reasoned action (TRA), a structural model that facilitates the formation of hypotheses which relate users’ attitudes to privacy to subsequent behaviour. This model is assessed by analysing the responses to a questionnaire from a large sample of participants. This allows us to evaluate the general accuracy of the model and to compare culturally distinct subgroups (Saudi vs. Chinese) using partial least-squares analysis. Results show good support for all of our hypotheses and indicate that there are clear cultural effects. One of these effects is the inadequacy of privacy policies implemented by SMP providers, regarding culturally specific ethical concerns.

The global shipping industry faces many uncertainties which impact on how organisations within this sector will perform in the future. Research in the critical success factors which impact the global shipping industry in the digital era is lacking. This study plugs the gap in the literature by identifying four key critical success factors which are innovation capability, risk governance capability, leadership and strategic capability, and technological capability. In addition, this study also found three organisational performance measures that are useful for senior management teams within the industry, namely, financial performance, operational performance and marketing performance. The results were then triangulated and validated by the case study method using a global shipping organisation. The findings establish a set of critical success factors and the corresponding relationships between the identified critical success factors and the identified organisational performance measures. The paper also provides managerial insights for industry practitioners for defining, prioritising and allocating resources in order to improve organisational performance.

Generative Artificial Intelligence (GAI) has brought revolutionary changes to the world, enabling businesses to create new experiences by combining virtual and physical worlds. As the use of GAI grows along with the Metaverse, it is explored by academics, researchers, and industry communities for its endless possibilities. From ChatGPT by OpenAI to Bard AI by Google, GAI is a leading technology in physical and virtual business platforms. This paper focuses on GAI’s economic and societal impact and the challenges it poses. Businesses must rethink their operations and strategies to create hybrid physical and virtual experiences using GAI. This study proposes a framework that can help business managers develop effective strategies to enhance their operations. It analyzes the initial applications of GAI in multiple sectors to promote the development of future customer solutions and explores how GAI can help businesses create new value propositions and experiences for their customers, and the possibilities of digital communication and information technology. A research agenda is proposed for developing GAI for business management to enhance organizational efficiency. The results highlight a healthy conversation on the potential of GAI in various business sectors to improve customer experience.

Literature on participation in the informal food sector in cities of the Global South is conventionally characterized by a survivalist or opportunistic perspective. The main difference is that opportunists, in contrast to survivalists, are motivated by entrepreneurial choice rather than necessity and see opportunities for economic and social advancement in the sector. Recent studies in Brazil and India conclude that research on informal sector participation requires a “both/and” rather than “either/or” approach. The main problem this paper addresses is whether the “both/and” model is also applicable in the African context. This is the first study to investigate the issue in the informal food sector of an African city; in this case, the capital city of Namibia, Windhoek. The paper evaluates five potential ways of distinguishing between survivalist and opportunistic food vendors and concludes that entrepreneurial motivation (EM) provides the most useful set of metrics. Selected EM responses are then used to construct four regression models—two survivalist and two opportunistic—in order to determine which individual and business characteristics are most strongly and consistently associated with survivalism and opportunism. Few vendors are both survivalist and opportunistic in orientation. There is a possibility of survivalists becoming more opportunistic over time but the models do not confirm this hypothesis. Apart from differences in EM, there are many similarities between the two groups and both would therefore benefit from a more enabling policy environment. The primary distinguishing business characteristic is the enterprise type with street food vendors most likely to be opportunistic. Ironically, it is street vendors who are seen as unsightly, unhealthy, and uncontrollable, and face the most difficult operating environment.

The purpose of this paper is to identify whether a person’s entrepreneurial characteristics can be sought in written texts, and it deals with aspects such as understanding hidden feelings by looking only at a person’s written texts. The transcripts of their speeches, interviews, and press conferences were analyzed in terms of semantic categories of words that were included with the use of General Inquirer software, which was developed in Harvard in order to support content analysis. Representatives clearly articulate and work together to establish the organization’s values, ascertain its objectives, create and carry out a strategic plan, and cultivate and interact with the organization’s perspective. A leader needs to be appointed, but they also need to have the ability to demonstrate their aspirations, excite their audience, and visualize their followers. In addition to this, they need to have the determination to take risks. This paper could be helpful for assessors of modern businesses as well as investigators of political and social sciences who are seeking to determine the perspective that is encountered in the literary documents of a particular person. A large variety of written documents from numerous people that have lived in a variety of historical eras and countries, regardless of gender and educational or professional background were examined. The common characteristic is that they were leading personalities in any of the following categories: their country, social movements, sports, art, business and social life.

The sharing economy has risen rapidly in the past decade. The development of shared accommodation encourages more hotels and guesthouses to attract customers through online accommodation-sharing platforms, which has become a meaningful way to fight against the competition of the traditional hotel industry. In this condition, what the hosts are concerned about most is how to attract customers’ attention through the platform display to increase reservations. Based on construal level theory, this paper explores how hosts’ information displayed on online accommodation-sharing platforms determines consumers’ booking behavior by influencing their psychological distance. We use machine learning methods to mine the raw data and extract the representational factors of psychological distance. Based on the data-driven behavior decision-making approach, we collected valid large-scale fine-grained secondary actual consumption data from Airbnb, the world’s leading online accommodation-sharing platform, and scientifically and intelligently processed the data using machine learning methods, then tested the hypotheses using the regression analysis software STATA15. Our findings suggest that both social distance and temporal distance have a negative impact on booking behavior of guests. In detail, subject diversity, perspective taking, and facial attractiveness in the dimension of social distance positively influence guests’ booking behavior; instant bookable in the temporal distance dimension positively affects booking behavior, while response time has a negative effect. This study contributes to the literature by empirically examining psychological distance in the booking behavior of guests in shared accommodation through the processing and analysis of actual consumption data. The findings have important practical implications for how shared accommodation service providers and sharing economy platform managers can operate better.

To reduce resource loss and environmental pollution, green CLSC has become a hot issue that manufacturing enterprises pay attention to. In green CLSC, manufacturers would pay attention to the fairness of profit distribution when making sales efforts. Therefore, this paper studies a green closed-loop supply chain (CLSC) considering manufacturer sales efforts and fairness concerns. Then, the centralized model and decentralized model are built and analyzed. Afterward, a profit-sharing contract between members is designed to coordinate the supply chain. We made the following observations: (1) The manufacturers’ fairness concerns would reduce product green degree, sales effort and recycling rate of used products, which is not conducive to the sustainable development of the green closed-loop supply chain. (2) When the manufacturers’ fairness concerns are gradually strengthened, the optimal decisions would deviate even more from the optimal equilibrium results. (3) When the coefficient of fairness concerns and the ratio of profit-sharing satisfy a certain range, Pareto improvement can be effectively realized.