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Recent advancements in Artificial Intelligence (AI) and, at its core, Machine Learning (ML) offer opportunities for organizations to develop new or enhance existing capabilities. Despite the endless possibilities, organizations face operational challenges in harvesting the value of ML-based capabilities (MLbC), and current research has yet to explicate these challenges and theorize their remedies. To bridge the gap, this study explored the current practices to propose a systematic way of orchestrating MLbC development, which is an extension of ongoing digitalization of organizations.

Data were collected from Finland's Artificial Intelligence Accelerator (FAIA) and complemented by follow-up interviews with experts outside FAIA in Europe, China and the United States over four years. Data were analyzed through open coding, thematic analysis and cross-comparison to develop a comprehensive understanding of the MLbC development process.

The analysis identified the main components of MLbC development, its three phases (development, release and operation) and two major MLbC development challenges: Temporal Complexity and Context Sensitivity. The study then introduced Fostering Temporal Congruence and Cultivating Organizational Meta-learning as strategic practices addressing these challenges.

This study offers a better theoretical explanation for the MLbC development process beyond MLOps (Machine Learning Operations) and its hindrances. It also proposes a practical way to align ML-based applications with business needs while accounting for their structural limitations. Beyond the MLbC context, this study offers a strategic framework that can be adapted for different cases of digital transformation that include automation and augmentation of work.

The rapid growth of emerging market firms (EMFs) has been a topic of interest for the past two decades, especially in China. However, few studies have discussed how and why EMFs can impel the upgrading of their capabilities to quickly win competitive advantages in the global market. In this context, the purpose of this paper is to unravel the implausible upgrading phenomenon from the perspective of technological proximity.

This paper adopts a single case study, specifically that of a leading Chinese e-bike firm, with a special focus on the dynamic nature of the capability upgrading process and underlying mechanisms.

The results show that taking advantage of technological proximity is an important way for EMFs to climb the ladder of capability upgrading. The stage-based process reveals how capability upgrading is achieved through elaborate actions related to technological proximity. Furthermore, this study finds three learning mechanisms behind the technological proximity, which enable firms to successfully upgrade to higher levels of capabilities. In particular, the trigger role played by contextual conditions in guiding firms' capability upgrading is highlighted and characterized.

This study enriches traditional capability upgrading literature from a technological proximity perspective, especially the traditional static upgrading research related to EMFs. The authors also contribute to the conceptualization of technological proximity. However, the research setting is China's e-bike industry; therefore, the study's generalizability to other emerging markets and industries may be limited.

The results show that it is important to recognize the value of the transfer and sharing of technology between proximal industries for local governments. Also, appropriate policies should be developed to break down the technology barriers between these industries. Moreover, rather than catching up with the superior technologies of multinational corporations in advanced countries, focusing on products with high technological proximity in local or regional areas may be more helpful for EMFs' upgrading.

This paper investigates the capability upgrading process and mechanisms in EMFs, particularly with respect to the role played by technological proximity.

This systematic review paper aims to examine extant empirical research involving educational technology during COVID-19 to provide an aggregated analysis of how the pandemic has influenced educational technology research.

Using a Preferred Reporting Items for Systematic Reviews and Meta-Analysis systematic review and an integrative review methodology, 50 primary research studies were selected from ten top-ranked educational research journals. These studies were reviewed regarding research purposes, methodologies, instruments, educational level, geographical distribution, and findings of the studies.

The findings reveal four emerging themes: influencing factors, effectiveness, challenges and teachers. The majority of the studies focused on higher education. Quantitative research design based on a questionnaire was the most adopted method of investigation by researchers.

Search parameters focused on the top 10 journals in the field of educational technology. Although this provides a level of quality, it narrowed the search.

For practitioners and researchers, this study provides a summary of the field to better understand what knowledge we have gained on the use of educational technology to enable a more agile, knowledgeable response to education in future emergencies.

This systematic review is unique in examining how the pandemic has influenced educational technology research. It also provides insight into gaps in the research that future researchers can use as a springboard to enable a more knowledge and a more agile approach to future emergencies.

This paper aims to explore the effect of fairness concerns on supply chain members' optimal decisions and profits, to compare their profits under different policies, and to investigate the impact of each policy on members, consumers, and the environment with fairness concerns.

Considering government policies and fairness concerns in recycling management, this paper develops five recycling and remanufacturing decision models (anarchy policy model, reward-penalty mechanism model, recycling investment subsidies model, government tax model, and fund subsidy system model). In each model, the manufacturer and the online platform form the Stackelberg game. This research further discusses comprehensive environmental benefits and consumer surplus under five scenarios.

First, the fairness concerns of the online platform inhibit the recovery rate and supply chain members' profit while increasing the platform's utility. Second, fairness concerns increase the profit gap between the manufacturer and online platform, and the higher the degree of fairness concerns, the greater the profit gap; however, the four policies reduce the profit gap. Finally, when there are fairness concerns, environmental taxes damage the interests of supply chain members and consumers, but are most beneficial to the environment; recycling investment subsidies are on the contrary; the fund subsidy system depends on the relative size of the treatment fund and the subsidy fund.

This paper provides useful insights on how to regulate government policy to improve supply chain management with fairness concerns.

This paper offers an overview of the burgeoning literature on open banking, focusing on its implications for the financial sector.

The paper reviews the recent developments in the nascent literature of open banking. In particular, it discusses the following issues. (1) the extent to which open banking fosters competition, drives innovation and enhances financial inclusion; (2) the impact of institutional arrangements on the outcomes of open banking initiatives and (3) the critical role of government in promoting open banking and regulating banking activities.

The paper concludes with a discussion on potential directions for future research. First, open banking introduces significant challenges to the traditional banking model. Furthermore, the interplay between open banking and financial risk presents an area ripe for exploration. Lastly, the importance of consumer education in the context of open banking cannot be overstated.

Open innovation enables financial institutions generate productive innovations as well as provide customers with significantly better services, by getting access to previously restricted customer data. However, currently non-bank and fintech lenders often face significant barriers in accessing comprehensive customer data, which restricts their capacity to support non-standard credit models. More emphasis is required to be assigned to research on the economic impact of open banking.