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This study views Net-Zero as a dynamic capability for decarbonising supply chains (SCs). This study aims to investigate the relationship between three information processing-related capabilities (supply chain visibility [SCV], supply chain integration [SCI] and big data analytics [BDA]) as its antecedents and SC performance as its competitive advantage outcome.

The authors conceptualise a research model grounded in the literature based on dynamic capabilities and information processing views. The study uses a structural equation modelling technique to test the hypotheses’ relationship using the survey data from 311 industrial enterprises.

The results show that SCI and BDA positively and directly influence the Net-Zero capability (NZC). No significant direct impact is found between SCV and NZC. BDA fully mediates SCV and partially mediates SCI in their relationship with NZC. The results also confirm that NZC positively impacts SC performance (SCP).

This study contributes to operations management and SC literature by extending the knowledge about Net-Zero SCs through an empirical investigation. In particular, the study suggests BDA is essential to enhance NZC as SCV alone does not significantly contribute. The study also documents the benefit of NZC on SCP, which can encourage more volunteer actions in the industry.

This paper aims to provide a discussion on the interface and interactions between data, analytical techniques and impactful research in humanitarian health supply chains. New techniques for data capturing, processing and analytics, such as big data, blockchain technology and artificial intelligence, are increasingly put forward as potential “game changers” in the humanitarian field. Yet while they have potential to improve data analytics in the future, larger data sets and quantification per se are no “silver bullet” for complex and wicked problems in humanitarian health settings. Humanitarian health supply chains provide health care and medical aid to the most vulnerable in development and disaster relief settings alike. Unlike commercial supply chains, they often lack resources and long-term collaborations to enable learning from the past and to improve further.

Based on a combination of the authors’ research experience, interactions with practitioners throughout projects and academic literature, the authors consider the interface between data and analytical techniques and highlight some of the challenges inherent to humanitarian health settings. The authors apply a systems approach to represent the multiple factors and interactions between data, analytical techniques and collaboration in impactful research.

Based on this representation, the authors discuss relevant debates and suggest directions for future research to increase the impact of data analytics and collaborations in fostering sustainable solutions.

This study distinguishes itself and contributes by bringing the interface and interactions between data, analytical techniques and impactful research together in a systems approach, emphasizing the interconnectedness.

This essay aims to draw attention to the idea of a new research approach to knowledge management (KM) labelled responsible KM (rKM) as a possible answer to not only address the consequences of the pandemic but also other present and upcoming societal challenges.

This essay has been prepared by a KM researcher who shares their own personal views and opinion regarding past and current societal developments and based on that offers a potentially new KM direction.

Switching the focus to rKM may help address current and upcoming social challenges that can only be addressed jointly by the global community and which would also involve a new consideration of the “knowledge” resource.

The essay proposes a new alternative approach to KM called rKM that is based on ideas that to the author’s knowledge have not been discussed in this way in the contemporary literature on KM.

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

This paper aims to examine the time-varying preferences for environment, social and corporate governance (ESG) investing in an emerging market. The investors seek ESG-conscious investments during a positive economic outlook, reflecting the time-varying nature of ESG demand. Specifically, the author shows that high-ESG stocks have negative abnormal returns during bad economic times but turn into positive abnormal returns in good economic times. The author also suggests that the alpha spread between high-ESG and low-ESG stocks is larger in good economic times than in bad times. Furthermore, individual investors prefer high ESG scoring stocks in good economic times. The author highlights that this ESG premium is shaped by economic projection and the households' financial wealth.

This study aims to investigate the effect of innovation on environmental, social and governance (ESG) performance and, consequently, its influence on the economic and financial performance of companies.

A quantitative and descriptive research was carried out based on secondary data from the Refinitiv Eikon^® database, using the panel data regression technique, considering the constructs: innovation, ESG performance and economic and financial performance.

The results showed that companies that tend to invest more financial resources in R&D are more likely to have higher ESG performance. In addition, companies that have higher ESG performance tend to have higher economic and financial performance.

Managers may consider investing more resources in R&D to achieve superior ESG performance. They should be aware that ESG is a strategic tool for creating financial and nonfinancial value for the organization. More than the traditional preparation of a financial report, stakeholders demand another type of information: ESG information.

The results confirm the basis of Stakeholder Theory, showing that the companies that meet the needs of all stakeholders tend to have greater economic and financial performance. ESG practices can include keeping employees motivated to work, improved corporate image in the eyes of customers, more satisfied suppliers and community and environment aligned with management. Therefore, these ESG initiatives are instrumental in protecting organizational objectives as well as increasing shareholder value.

The purpose of this article is to summarize three Luhmannian critiques on morality, illustrate new roles for morality and add constructive interpretations.

Luhmann has recently been described as downright negative toward morality, resulting in a refusal to use ethics as a sociologist, thus leading to a limited use of his theory in moral issues. A constructive interpretation could support a more functional use of morality in social system theory.

First, Luhmann signals that morality can no longer fulfill its integrative function in society but also that society has recourse to moral sensitivity. Second, Luhmann describes how anxiety is crucial in modern morality and indicates which role risk and danger could play. The author builds further on this and proposes the concept of “social system attention” that can provide answers to individual and organizational anxiety. The author proposes that institutionalized socialization can support an integrative morality. Third, Luhmann states that ethics today is nothing more than a utopia but also that the interdiction of moral self-exemption is an essential element. The author adds that a relational ontology for social systems theory can avoid ethics as utopia.

This article is a programmatic plea to further elaborate morality from a system theory perspective in which meaning is relationally positioned.

This article could potentially provide a more functional application of morality in social systems, thus leading to improvements of attempts of ethical decision-making. The originality of the approach lies in the interpretation of basic assumptions of Luhmann social system theory that are not core to his theory.