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In recent years, new and technologically innovative financial products and services, generally subsumed under the fintech umbrella, have permeated all areas of capital markets at an exponential rate. Primarily driven by developments in Web3 and advancements in artificial intelligence (AI), fintech solutions offer valuable benefits to all existing markets and participants and are the basis for introducing wholly new segments to classic capital market ecosystems. However, this increasing fintech adaptation does not come without challenges. Due to the technologies' nascent nature and often unregulated status, many products are susceptible to manipulation and fraud. The result can be sizable investor losses and excessive regulatory and public scrutiny. This chapter highlights the most essential and prominent fintech solutions used in capital markets today, along with their features, value additiveness, and degree of adaptation.

The literature mainly concentrates on the relationships between externally oriented digital transformation (ExtDT), big data analytics capability (BDAC) and business model innovation (BMI) from an intra-organizational perspective. However, it is acknowledged that the external environment shapes the firm's strategy and affects innovation outcomes. Embracing an external environment perspective, the authors aim to fill this gap. The authors develop and test a moderated mediation model linking ExtDT to BMI. Drawing on the dynamic capabilities view, the authors' model posits that the effect of ExtDT on BMI is mediated by BDAC, while environmental hostility (EH) moderates these relationships.

The authors adopt a quantitative approach based on bootstrapped partial least square-path modeling (PLS-PM) to analyze a sample of 200 Italian data-driven SMEs.

The results highlight that ExtDT and BDAC positively affect BMI. The findings also indicate that ExtDT is an antecedent of BMI that is less disruptive than BDAC. The authors also obtain that ExtDT solely does not lead to BDAC. Interestingly, the effect of BDAC on BMI increases when EH moderates the relationship.

Analyzing the relationships between ExtDT, BDAC and BMI from an external environment perspective is an underexplored area of research. The authors contribute to this topic by evaluating how EH interacts with ExtDT and BDAC toward BMI.

To fill the gap, this article examines the inter-governmental collaboration mechanisms behind the platform curtain.

Behind the curtain is to look at what makes things happen backstage. For collaborative e-governance platforms, scholars have assumed that technological factors and user characteristics are the determinants for platform success. Little attention has been paid to the issue of how multiple governments, acting as platform co-builders and co-operators, interact and collaborate backstage to provide integrated e-services.

Based on data from survey questionnaires sent to government employees, the results show that governments’ information processing capacities cannot directly affect collaboration; however, these capacities can impact collaboration via the mediating variable of horizontal relations. In addition, we found that higher-ranking authorities are better suited to intervene once horizontal relations have been established and that more adaptable organizations are better at forming horizontal relations with peers. For governments participating in collaborative e-governance platforms, our findings are practically applicable.

The research question reads as: How do various government departments acting as platform co-builders and co-operators judge their collaboration performance, and what collaboration mechanisms contribute to it? We study this research question by constructing a conceptual model based on the Organizational Information Processing Theory (OIPT) and the Collaborative Governance Theory (CGT), both suggesting information processing capacities, organizational flexibility, horizontal relations and vertical intervention as indispensable factors influencing collaboration performance in ICT-supported groupwork. We propose and test four hypotheses on the relationships among these four factors to reveal the inter-governmental collaboration mechanisms for cross-government platformisation projects.

This paper aims to examine the financial outcome of reverse logistics among firms in a developing country. The authors draw on the organizational information processing theory to propose that analytics capability moderates the relationship between reverse logistics and financial performance.

The authors collected firm-level survey data from 200 manufacturing firms in Ghana, a developing country in sub-Saharan Africa. Partial least squares structural equations modeling is used to examine the proposed relationships, and the moderating effects are further probed using Hayes PROCESS.

The empirical results show that reverse logistics is negatively related to financial performance. However, analytics capability attenuates this negative relationship, such that firms with high analytics capability obtain a positive relationship between reverse logistics and financial performance.

Firms in developing countries should combine their reverse logistics strategies with developing analytics capabilities that help minimize uncertainties and increase the efficient collection and use of information to reduce the cost of reverse logistics.

This paper examines how reverse logistics relates to financial performance in low-resource contexts. Beyond the novelty of the context, it explores the information processing needs of reverse logistics systems and provides empirical data to support analytics capability. This has yet to be considered in prior studies.

Process mining (PM) has emerged as a leading technology for gaining data-based insights into organizations’ business processes. As processes increasingly cross-organizational boundaries, firms need to conduct PM jointly with multiple organizations to optimize their operations. However, current knowledge on cross-organizational process mining (coPM) is widely dispersed. Therefore, we synthesize current knowledge on coPM, identify challenges and enablers of coPM, and build a socio-technical framework and agenda for future research.

We conducted a literature review of 66 articles and summarized the findings according to the framework for Information Technology (IT)-enabled inter-organizational coordination (IOC) and the refined PM framework. The former states that within inter-organizational relationships, uncertainty sources determine information processing needs and coordination mechanisms determine information processing capabilities, while the fit between needs and capabilities determines the relationships’ performance. The latter distinguishes three categories of PM activities: cartography, auditing and navigation.

Past literature focused on coPM techniques, for example, algorithms for ensuring privacy and PM for cartography. Future research should focus on socio-technical aspects and follow four steps: First, determine uncertainty sources within coPM. Second, design, develop and evaluate coordination mechanisms. Third, investigate how the mechanisms assist with handling uncertainty. Fourth, analyze the impact on coPM performance. In addition, we present 18 challenges (e.g. integrating distributed data) and 9 enablers (e.g. aligning different strategies) for coPM application.

This is the first article to systematically investigate the status quo of coPM research and lay out a socio-technical research agenda building upon the well-established framework for IT-enabled IOC.

This study examines green absorptive capacity as an important intervening variable that elucidates the relationship between green supply chain management (GSCM) practices (specifically, green purchasing, customer cooperation and investment recovery) and firm performance.

Drawing from the theoretical underpinnings of the natural-resource-based view theory and information processing theory, a research model is developed and tested using data obtained from 368 manufacturing firms in Ghana. Data analysis was conducted using structural equation modeling.

The results indicate that green purchasing, customer cooperation and investment recovery have a direct positive and significant effect on firm performance. Additionally, green purchasing and customer cooperation have a positive and significant effect on green absorptive capacity but investment recovery does not. Further, the results show that the paths from green purchasing and customer cooperation to firm performance are positively mediated by green absorptive capacity.

The study reveals to supply chain managers that green absorptive capacity is an important conduit through which firms can achieve enhanced firm performance from GSCM initiatives.

This study makes a contribution by integrating the absorptive capacity literature and green management literature and establishes green absorptive capacity as a mechanism through which GSCM practices enhance firm performance.

Managers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team interacts when making BMI decisions. The paper also investigates how group biases and board members’ risk willingness affect this process.

Empirical data were collected through 26 in-depth interviews with German managing directors from 13 companies in four industries (mobility, manufacturing, healthcare and energy) to explore three research questions: (1) What group effects are prevalent in BMI group decision-making? (2) What are the key characteristics of BMI group decisions? And (3) what are the potential relationships between BMI group decision-making and managers' risk willingness? A thematic analysis based on Gioia's guidelines was conducted to identify themes in the comprehensive dataset.

First, the results show four typical group biases in BMI group decisions: Groupthink, social influence, hidden profile and group polarization. Findings show that the hidden profile paradigm and groupthink theory are essential in the context of BMI decisions. Second, we developed a BMI decision matrix, including the following key characteristics of BMI group decision-making managerial cohesion, conflict readiness and information- and emotion-based decision behavior. Third, in contrast to previous literature, we found that individual risk aversion can improve the quality of BMI decisions.

This paper provides managers with an opportunity to become aware of group biases that may impede their strategic BMI decisions. Specifically, it points out that managers should consider the key cognitive constraints due to their interactions when making BMI decisions. This work also highlights the importance of risk-averse decision-makers on boards.

This qualitative study contributes to the literature on decision-making by revealing key cognitive group biases in strategic decision-making. This study also enriches the behavioral science research stream of the BMI literature by attributing a critical influence on the quality of BMI decisions to managers' group interactions. In addition, this article provides new perspectives on managers' risk aversion in strategic decision-making.

Notwithstanding their potential benefits especially for individuals with low health literacy, users are still somewhat skeptical about the reliability of healthcare chatbots. The present study aims to address this challenge by investigating strategies to enhance users’ cognitive and emotional trust in healthcare chatbots. Particularly, this study aims to understand the effects of chatbot design cues in increasing trust and future chatbot use intention for low health literacy users.

We conducted two experimental studies with a final sample of 327 (Study 1) and 241 (Study 2). Three different chatbots were developed (Chatbot design: Bot vs Male-doctor vs Female-doctor). Participants were asked to have a medical consultation with the chatbot. Participants self-reported their health literacy scores. The PROCESS model 7 was used to analyze the hypotheses.

The results showed that the female-doctor cues elicited greater cognitive and emotional trust, whereas the male-doctor cues only led to greater cognitive trust (vs bot-like cues). Importantly, this study found that users’ health literacy is a significant moderating factor in shaping cognitive and emotional trust. The results indicated that both the female and male-doctor cues’ positive effects on cognitive trust were significant for those with lower levels of health literacy. Furthermore, the positive effect of the female-doctor cues on emotional trust was also significant only for those whose health literacy level was low. The increased cognitive and emotional trust led to greater future intention to use the chatbot, confirming significant moderated mediation effects.

Despite the strong economic and educational benefits of healthcare chatbots for low health literacy users, studies examining how healthcare chatbot design cues affect low health literate users surprisingly remained scarce. The results of this study suggest that healthcare chatbots can be a promising technological intervention to narrow the health literacy gap when aligned with appropriate design cues.

To predict the price of battery-grade lithium carbonate accurately and provide proper guidance to investors, a method called MFTBGAM is proposed in this study. This method integrates textual and numerical information using TCN-BiGRU–Attention.

The Word2Vec model is initially employed to process the gathered textual data concerning battery-grade lithium carbonate. Subsequently, a dual-channel text-numerical extraction model, integrating TCN and BiGRU, is constructed to extract textual and numerical features separately. Following this, the attention mechanism is applied to extract fusion features from the textual and numerical data. Finally, the market price prediction results for battery-grade lithium carbonate are calculated and outputted using the fully connected layer.

Experiments in this study are carried out using datasets consisting of news and investor commentary. The findings reveal that the MFTBGAM model exhibits superior performance compared to alternative models, showing its efficacy in precisely forecasting the future market price of battery-grade lithium carbonate.

The dataset analyzed in this study spans from 2020 to 2023, and thus, the forecast results are specifically relevant to this timeframe. Altering the sample data would necessitate repetition of the experimental process, resulting in different outcomes. Furthermore, recognizing that raw data might include noise and irrelevant information, future endeavors will explore efficient data preprocessing techniques to mitigate such issues, thereby enhancing the model’s predictive capabilities in long-term forecasting tasks.

The price prediction model serves as a valuable tool for investors in the battery-grade lithium carbonate industry, facilitating informed investment decisions. By using the results of price prediction, investors can discern opportune moments for investment. Moreover, this study utilizes two distinct types of text information – news and investor comments – as independent sources of textual data input. This approach provides investors with a more precise and comprehensive understanding of market dynamics.

We propose a novel price prediction method based on TCN-BiGRU Attention for “text-numerical” information fusion. We separately use two types of textual information, news and investor comments, for prediction to enhance the model's effectiveness and generalization ability. Additionally, we utilize news datasets including both titles and content to improve the accuracy of battery-grade lithium carbonate market price predictions.

Research on online user privacy shows that empirical evidence on how privacy literacy relates to users' information privacy empowerment is missing. To fill this gap, this paper investigated the respective influence of two primary dimensions of online privacy literacy – namely declarative and procedural knowledge – on online users' information privacy empowerment.

An empirical analysis is conducted using a dataset collected in Europe. This survey was conducted in 2019 among 27,524 representative respondents of the European population.

The main results show that users' procedural knowledge is positively linked to users' privacy empowerment. The relationship between users' declarative knowledge and users' privacy empowerment is partially supported. While greater awareness about firms and organizations practices in terms of data collections and further uses conditions was found to be significantly associated with increased users' privacy empowerment, unpredictably, results revealed that the awareness about the GDPR and user’s privacy empowerment are negatively associated. The empirical findings reveal also that greater online privacy literacy is associated with heightened users' information privacy empowerment.

While few advanced studies made systematic efforts to measure changes occurred on websites since the GDPR enforcement, it remains unclear, however, how individuals perceive, understand and apply the GDPR rights/guarantees and their likelihood to strengthen users' information privacy control. Therefore, this paper contributes empirically to understanding how online users' privacy literacy shaped by both users' declarative and procedural knowledge is likely to affect users' information privacy empowerment. The study empirically investigates the effectiveness of the GDPR in raising users' information privacy empowerment from user-based perspective. Results stress the importance of greater transparency of data tracking and processing decisions made by online businesses and services to strengthen users' control over information privacy. Study findings also put emphasis on the crucial need for more educational efforts to raise users' awareness about the GDPR rights/guarantees related to data protection. Empirical findings also show that users who are more likely to adopt self-protective approaches to reinforce personal data privacy are more likely to perceive greater control over personal data. A broad implication of this finding for practitioners and E-businesses stresses the need for empowering users with adequate privacy protection tools to ensure more confidential transactions.