Smart grids (SGs) revolutionize existing power grids by using a wide range of developing disruptive technologies to generate clean, efficient, and predictable energy. Our study uses an action research method and focuses solely on the first two stages of the action research process, diagnosis and action planning, to evaluate ways to adopt artificial intelligence (AI) applications in SGs for predictive analytics in practice. The diagnosis stage of the study entails conducting a systematic literature review on AI applications in SGs, highlighting four areas of potential for predictive analytics: power outage prediction, demand response, control and coordination, and AI-enabled security to optimize decision-making, diagnose faults, and improve grid stability and security. The action planning step included a document analysis to devise methods to enable the practical implementation of AI in smart grids for predictive analytics. Finally, we address practical ways for implementing transparent AI for predictive analytics, followed by a conclusion and future research direction. The study’s key conclusion is that more research is needed to complete the action taking (implementing the solution), evaluation (assessing the results), and learning (reflecting on lessons learned) phases of the action research cycle.

The shift toward decentralised energy generation, driven by the rapid expansion of renewable energy sources, is redefining traditional consumers as active co-creators of electricity generation. This study examines the role of predictive analytics in supporting resilience and co-creation within distributed energy resources (DERs). It employs semi-structured qualitative interviews to explore stakeholder perspectives on how predictive analytics is envisioned and applied. Our preliminary findings show that predictive analytics could be used across diverse applications, such as forecasting, maintenance, and load optimisation. These diverse applications actively shape energy management systems while influencing decision-making and operational design. The application of predictive analytics is shaped by both enabling and constraining conditions. We conclude that predictive analytics functions not only as a technical tool but also aligns stakeholders and promotes collaborative and adaptive energy management among distributed energy resources.

Smart grids (SG) transform a traditional electricity energy grid by incorporating many emergingdisruptive technologies to produce clean, efficient, and dependable energy. This review focusesexclusively on one instance of AI application in SG - predictive analytics. We conducted asystematic literature review on AI applications in SG, which resulted in a review of 18 articlespublished after 2015. In the first part of the review, it is concluded that integrating AI into SGcould address many challenges in SGs and transform traditional grids. The second part focuseson the predictive analytic capability enabled through AI in SG. Predictive analytics can be appliedin many contexts to optimize decision-making, diagnose faults, and enhance grid stability. Thelast part presents two use cases for AI-enabled predictive analytics: energy outage prediction andsecurity enhancement. AI, especially the predictive analytic technique, is a future avenue for SGenhancement. The main conclusion from the review is that more research describing empiricalexamples of the adoption and deployment of AI predictive analytics in SG is needed.

mHealth is believed to be a strategic solution to Thailand’s healthcare problems. This research intendsto investigate causes for the (non)-adoption of MDme, an anonymized Thai mHealth application forself-care. Our current literature review indicates two research gaps: (1) a lack of qualitative study, and(2) a limited body of research on mHealth (non)-adoption in Thailand.

Arguments to invest in Accounting Information Systems (AIS) are multiple, but two key themes have emerged in the literature: the potential for cost saving from improved efficiency and the capacity to support business strategies. This chapter examines the latter of these two themes, taking as its starting point Porter’s two generic business strategies: cost leadership and differentiation. Two approaches to AIS acquisition are considered: bespoke development and off-the-shelf purchase. These two dimensions – generic business strategy and software acquisition mode – provide a framework for analysis of AIS support for firms and the four quadrants of the framework provide the basis for this chapter. A key contribution of the chapter is the identification of the critical factors involved in the choice of AIS to support the business strategy. The chapter also considers the implications for AIS of new forms of business competition afforded by platforms, networks and ecosystems and new modes of transferring and recording value through cryptocurrencies and blockchains.

In recent years, the increasing adoption of digital technologies have dramatically accelerated the speed of change in the accounting information systems domain. However, AISs go back centuries. This chapter provides a brief summary of the history of accounting information systems, from when the first rudimentary versions of AIS were adopted to the information age with the adoption of mainframes and ERP systems to the present with the increasing adoption of business intelligence and data analytics capabilities. The technological development of AIS is also discussed in relation to how the role of accountants has changed over time. 

Technological innovations are re-writing the norm of payments throughout the world. In the past few years, several innovative payment solutions have been continuously introduced into the market. This research is interested in an innovation named biometric payment card, which is a card payment using a combination of token, i.e., the card itself, and biometric authentications, i.e., fingerprints. Card owners are required to authenticate in-person card transactions with a fingerprint placed onto the card. In Sweden, there is an exceptionally high level of payment card penetration rate, i.e., at 97 percent (Riksbank 2019). There is also a high amount of yearly average payment card transaction per user at 319, compared to the EU average at 116 (Riksbank 2019). Therefore, a card-based payment solution is argued to be a relevant choice for the Swedish market despite growth in other technological-driven payment categories, especially mobile payments. Biometric payment card will arrive in Sweden for pilot testing in 2021. Hence, it is crucial to study a behavioral intention innovation to accept this technology among prospective users in Sweden to ensure a smooth transition to the biometric technology. This study plans to explore a behavioral intention to accept biometric payment cards. It will combine classical technology acceptance model (TAM) constructs - like perceived ease of use (PEOU), perceived usefulness (PU) and attitude (ATT) - with trust (T). Since the biometric payment cards are not yet implemented in Sweden for the time being, an online survey will be designed in a way that respondents are required to watch a 30 second video instruction on how the biometric payment card will work once it is implemented. Structural equation modelling (SEM) will be used for data analysis. This study will contribute specifically to the academic research on biometric card innovation acceptance. To the best of our knowledge, it is believed to be the first study that provides an acceptance insight on this innovation. Second, this study will contribute to an importance of trust in the context of biometric technologies. For practitioners, this study will provide an insight toward factors that should be considered to promote biometric card acceptance among Swedish consumers.

References:

Riksbank. 2019. “Payments in Sweden 2019.” https://www.riksbank.se/globalassets/media/rapporter/sabetalar-svenskarna/2019/engelska/payments-in-sweden-2019.pdf