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Consumers' dramatic demand has a pernicious effect throughout the supply chain. It exacerbates inventory distortion because of significant revenue loss caused by stock-level issues. Despite the availability of several forecasting techniques, large organisations, manufacturing firms, and e-commerce websites collectively lose around $1.8 trillion annually to inventory distortion. If this problem is solved, sales may increase by 10.3 percent. The businesses are concerned about mitigating this loss. Artificial intelligence (AI) can play a significant role in building resilient supply chains. However, developing AI models consumes time and cost. In this paper, we propose a No Code Artificial Intelligence (NCAI) enabling non-technical companies to build machine learning models based on production quantity and inventory replenishment. The development of the NCAI model is fast and inexpensive. However, little research deals with applying NCAI to operations and supply chain problems. Addressing the existing gap, we show the application of NCAI in the retail industry. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence (AI) in the healthcare field is gaining popularity. However, it also raises some concerns related to privacy and ethical aspects that require the development of a responsible AI framework. The principle of responsible AI states that artificial intelligence-based systems should be considered a part of composite societal and technological systems. This study attempts to establish whether AI risks in digital healthcare are positively associated with responsible AI. The moderating effect of perceived trust and perceived privacy risks is also examined. The theoretical model was based on perceived risk theory. Perceived risk theory is important in the context of this study, as risks related to uneasiness and uncertainty can be expected in the development of responsible AI due to the volatile nature of intelligent applications. Our research provides some interesting findings which are presented in the discussion section. [ABSTRACT FROM AUTHOR]

This study aims to investigate the role of artificial intelligence (AI) driven facial recognition to enhance a value proposition by influencing different areas of services in the travel and tourism industry. We adopted semi-structured interviews to derive insights from 26 respondents. Thematic analysis reveals the development of four main themes (personalization, data-driven service offering, security and safety, and seamless payments). Further, we mapped the impact of AI- driven facial recognition to enhance value and experience for corporate guests. Findings indicate that AI-based facial recognition can facilitate the travel and tourism industry in understanding travelers' needs, optimization of service offers, and value-based services, whereas data-driven services can be realized in the form of customized trip planning, email, and calendar integration, and quick bill summarization. This contributes to strengthening the tourism literature through the lens of organizational information processing theory. [ABSTRACT FROM AUTHOR]

Digitalization is globally transforming the world with profound implications. It has enormous potential to foster progress toward sustainability. However, in its current form, digitalization also continues to enable and encourage practices with numerous unsustainable impacts affecting our environment, ingraining inequality, and degrading quality of life. There is an urgent need to identify such multifaceted impacts holistically. Impact assessment of digital interventions (DIs) leading to digitalization is essential specifically for Sustainable Development Goals (SDGs). Action is required to understand the pursuit of short-term gains toward achieving long-term value-driven sustainable development. We need to understand the impact of DIs on various actors and in diverse contexts. A holistic understanding of the impact will help us align the visions of sustainable development and identify potential measures to mitigate negative short and long-term impacts. The recently developed digitainability assessment framework (DAF) unveils the impact of DIs with an in-depth context-aware assessment and offers an evidence-based impact profile of SDGs at the indicator level. This paper demonstrates how DAF can be instrumental in guiding participatory action for the implementation of digitainability practices. This paper summarizes the insights developed during the Digitainable Spring School 2022 (DSS) on "Sustainability with Digitalization and Artificial Intelligence," one of whose goals was to operationalize the DAF as a tool in the participatory action process with collaboration and active involvement of diverse professionals in the field of digitalization and sustainability. The DAF guides a holistic context-aware process formulation for a given DI. An evidence-based evaluation within the DAF protocol benchmarks a specific DI's impact against the SDG indicators framework. The participating experts worked together to identify a DI and gather and analyze evidence by operationalizing the DAF. The four DIs identified in the process are as follows: smart home technology (SHT) for energy efficiency, the blockchain for food security, artificial intelligence (AI) for land use and cover change (LUCC), and Big Data for international law. Each of the four expert groups addresses different DIs for digitainability assessment using different techniques to gather and analyze data related to the criteria and indicators. The knowledge presented here could increase understanding of the challenges and opportunities related to digitainability and provide a structure for developing and implementing robust digitainability practices with data-driven insights. [ABSTRACT FROM AUTHOR]

In recent decades, floods have led to massive destruction of human life and material. Time is of the essence for evacuation, which in turn is determined by early warning systems. This study proposes a wireless sensor network decision model for the detection of flood disasters by observing changes in weather conditions compared to historical information at a given location. To this end, we collected data such as air pressure, wind speed, water level, temperature and humidity (DH11), and precipitation (0/1) from sensors located at several points in the area under consideration and obtained sea level air pressure and rainfall from the Google API. The collected data was then transmitted via a LoRaWAN network implemented in Raspberry-Pi and Arduino. The developed support vector machine (SVM) model includes a number of coordinators responsible for a number of sectors (locations). The SVM model sends the binary decisions (flood or no flood) with an accuracy of 98% to a cloud server connected to monitoring rooms, where a decision can be made regarding the response to a possible flood disaster. [ABSTRACT FROM AUTHOR]

Purpose: The hospitality industry has witnessed numerous changes to enhance the stay experience of guests. To offer a memorable stay experience, the industry has started deploying intelligent robots. Therefore, this case study aims to examine and explore artificial intelligence (AI) enabled robots in hospitality industry in order to enhance guest experience in a smart city. Design/methodology/approach: Semistructured interviews have been conducted at Novotel Ambassador Seoul Dongdaemun Hotels and Residences, Seoul, South Korea, to understand the stay experience of guests regarding services offered by AI enabled robots. The authors have selected employees for interviews since employees listen and witness the guest experience directly. Out of 214 employees in the hotel with varied experience and background, 26 interviews are conducted. Findings: Through a systematic approach of coding, the authors have identified that deploying AI enabled robots facilitates the automation, information gathering, personalization and seamless service in the hospitality industry of a smart city. Further, with a back-and-forth mapping mechanism based on epistemological principles, the authors made four propositions that lead to the development of a research framework. Research limitations/implications: The practicing managers of hospitality industry can employ AI enabled robots within the scope of improving and automating the processes that can also offer increased personalization to enhance the stay experience, which is expected in a smart city. Originality/value: The study offers a unique contribution to literature, since it is a live case study, and the information is from the practicing employees of a well-known organization in a hospitality sector from a smart city (Novotel Ambassador Seoul Dongdaemun Hotels and Residences, Seoul, South Korea). [ABSTRACT FROM AUTHOR]

The domain of Artificial Intelligence (AI) ethics is not new, with discussions going back at least 40 years. Teaching the principles and requirements of ethical AI to students is considered an essential part of this domain, with an increasing number of technical AI courses taught at several higher-education institutions around the globe including content related to ethics. By using Latent Dirichlet Allocation (LDA), a generative probabilistic topic model, this study uncovers topics in teaching ethics in AI courses and their trends related to where the courses are taught, by whom, and at what level of cognitive complexity and specificity according to Bloom’s taxonomy. In this exploratory study based on unsupervised machine learning, we analyzed a total of 166 courses: 116 from North American universities, 11 from Asia, 36 from Europe, and 10 from other regions. Based on this analysis, we were able to synthesize a model of teaching approaches, which we call BAG (Build, Assess, and Govern), that combines specific cognitive levels, course content topics, and disciplines affiliated with the department(s) in charge of the course. We critically assess the implications of this teaching paradigm and provide suggestions about how to move away from these practices. We challenge teaching practitioners and program coordinators to reflect on their usual procedures so that they may expand their methodology beyond the confines of stereotypical thought and traditional biases regarding what disciplines should teach and how. [ABSTRACT FROM AUTHOR]

There exists literary evidence that there is an established relationship between humanizing bots and customer perceptions of fairness and consequential user experience. This study seeks to evaluate factors that impact the perception surrounding the fairness of social bots and the impact of these factors on user experience. A qualitative approach was used to examine the fairness factors related to social bots and their impact on user experience with the help of data collected from 32 professionals with diverse backgrounds. The results obtained from manual coding and NVivo were assessed to evaluate the convergence of themes for the confluence of results and establish thematic validity. The findings revealed two major themes namely, artificial intelligence (AI) technology governance and AI technology outcome. This research furthers user experience literature and provides a theoretical framework delineating antecedents (AI technology governance and AI technology outcomes) of positive user experience while deploying AI-based social bots. • Evaluate factors that impact the perception surrounding the fairness of social bots. • Qualitative data was collected from 32 professionals with diverse backgrounds. • Findings revealed two themes: AI technology governance and AI technology outcome. • Framework delineating antecedents of user experience while using AI-based social bots. • Explains impact of AI technology governance and AI technology outcome on user experience [ABSTRACT FROM AUTHOR]

The evolution of digital platforms and related ecosystems has led to a new and essential context of entrepreneurship. Previous studies have revealed that although high-tech new ventures possess cutting-edge technology, they face significant challenges because of vital resource constraints and limited management capability. Design thinking practices have emerged as a prominent problem-solving approach offering innovation benefits during high uncertainties. However, in the context of high-tech ventures, more research is needed on linking the relationship between design thinking principles, innovation theories (effectuation and causation), and firm performance. The present study investigates how the design thinking principles influence innovation theories and whether it leads to improved high-tech venture performance. Based on a survey of 291 platform-based start-ups in India, the results show that design thinking ways are well integrated into the theories of effectuation and causation, facilitating the growth of platform-based ventures. However, the influence of effectuation and causation on platform-based venture performance is found to be mediated through the digital capabilities acquired by these ventures. • This study assesses how the design thinking principles influence innovation theories. • Digital capabilities (DCs) are critical in new platform-based ventures to drive performance. • DCs mediate between effectuation and causation's effect on venture performance. • Design thinking ways are well integrated into the theories of effectuation and causation. • Platform-based venture performance is affected by the experience of the entrepreneur. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) should aim at benefiting society, the economy, and the environment, i.e., AI should aim to be socially good. The UN-defined Sustainable Development Goals (SDGs) are the best depiction to measure social good. For AI to be socially good, it must support all 17 UN SDGs. Our work provides a unique insight into AI on all fronts including Curricula, Frameworks, Projects, and Research papers. We then analyze these datasets to extract meaningful information for policymakers and researchers alike - shedding light on how AI is being used and can potentially be employed in the future to achieve the SDGs. To this end, we devised a methodology using keyword-matching and keyword-similarity to compute the relevance of the SDGs for a given document. SDG metadata and AI4SDG Projects (Oxford initiative on AI4SDGs) were used to validate our methodology. We find an imbalance of coverage with SDG 9 (Industry Innovation and Infrastructure) having the highest representation (with 50.3% of our data containing references to it) compared to SDGs 5, 6, 14, and 15, which have the lowest representation (5% of observed data). Findings from this study suggest that the development of AI technology is focused on improving the current economic growth, but it might neglect important societal and environmental issues. • We observe the link between Artificial Intelligence (AI) and Sustainable Development Goals (SDGs). • We use automated methodologies to find insights and overlaps between AI and the SDGs. • AI-Ethics frameworks need to give more attention to Society and Environment areas. • Inclusive action is needed to balance the efforts for solving SDGs by using AI. • SDGs 13, 14, and 15 (all related to the Environment area) are not sufficiently addressed. [ABSTRACT FROM AUTHOR]

Digitalization is widely recognized as a transformative power for sustainable development. Careful alignment of progress made by digitalization with the globally acknowledged Sustainable Development Goals (SDGs) is crucial for inclusive and holistic sustainable development in the digital era. However, limited reference has been made in SDGs about harnessing the opportunities offered by digitalization capabilities. Moreover, research on inhibiting or enabling effects of digitalization considering its multi-faceted interlinkages with the SDGs and their targets is fragmented. There are only limited instances in the literature examining and categorizing the impact of digitalization on sustainable development. To overcome this gap, this paper introduces a new Digitainability Assessment Framework (DAF) for context-aware practical assessment of the impact of the digitalization intervention on the SDGs. The DAF facilitates in-depth assessment of the many diverse technical, social, ethical, and environmental aspects of a digital intervention by systematically examining its impact on the SDG indicators. Our approach draws on and adapts concepts of the Theory of Change (ToC). The DAF should support developers, users as well policymakers by providing a 360-degree perspective on the impact of digital services or products, as well as providing hints for its possible improvement. We demonstrate the application of the DAF with the three test case studies illustrating how it supports in providing a holistic view of the relation between digitalization and SDGs. [ABSTRACT FROM AUTHOR]

• In the era of industry 4.0 and digital manufacturing, big data analytics is a game changer in increasing accuracy in decision-making. • Big data affects firms' marketing performance for firms involved in digital manufacturing as well as service. • Qualitative analysis and was guided by a knowledge-based theoretical framework. • Thematic analyses resulted in the identification of a number of emergent categories. A universal trend in advanced manufacturing countries is defining Industry 4.0, industrialized internet and future factories as a recent wave, which may transform the production and its related services. Further, big data analytics has emerged as a game changer in the business world due to its uses for increasing accuracy in decision-making and enhancing performance of sustainable industry 4.0 applications. This study intends to emphasize on how to support Industry 4.0 with knowledge based view. For the same, a conceptual model is framed and presented with essential components that are required for a real world implementation. The study used qualitative analysis and was guided by a knowledge-based theoretical framework. Thematic analysis resulted in the identification of a number of emergent categories. Key findings highlight significant gaps in conventional decision-making systems and demonstrate how big data enhances firms' strategic and operational decisions as well as facilitates informational access for improved marketing performance. The resulting proposed model can provide managers with a reference point for using big data to line up firms' activities for more effective marketing efforts and presents a conceptual basis for further empirical studies in this area. [ABSTRACT FROM AUTHOR]

• Effect of institutional pressures and resources on BDA-AI adoption is examined. • Effect of BDA-AI on sustainable manufacturing and circular economy capabilities. • Digital progress is essential for the development of circular economy capabilities. The significance of big data analytics-powered artificial intelligence has grown in recent years. The literature indicates that big data analytics-powered artificial intelligence has the ability to enhance supply chain performance, but there is limited research concerning the reasons for which firms engaging in manufacturing activities adopt big data analytics-powered artificial intelligence. To address this gap, our study employs institutional theory and resource-based view theory to elucidate the way in which automotive firms configure tangible resources and workforce skills to drive technological enablement and improve sustainable manufacturing practices and furthermore develop circular economy capabilities. We tested the research hypothesis using primary data collected from 219 automotive and allied manufacturing companies operating in South Africa. The contribution of this work lies in the statistical validation of the theoretical framework, which provides insight regarding the role of institutional pressures on resources and their effects on the adoption of big data analytics-powered artificial intelligence, and how this affects sustainable manufacturing and circular economy capabilities under the moderating effects of organizational flexibility and industry dynamism. [ABSTRACT FROM AUTHOR]

Rapidly increasing applications of Digitalization and Artificial Intelligence (D&AI) are already impacting our day-to-day life substantially, along with social and economic prospects worldwide. The accelerating utilization of D&AI has stirred the discussion concerning the responsible application of technologies for assisting the implementation of the Sustainable Development Goals (SDGs). D&AI can raise productivity, lower costs, reduce resource intensity, and enable efficient public services. However, there are also risks and downsides that we all must identify and tackle to address any potential short-/long-term undesired impact. Notably, there exists a gap in knowledge about the mutual relationships between D&AI and the 17 SDGs. To address this gap and gather broader perspectives of experts on the potential uses and pitfalls of D&AI for SDGs and their respective indicators, we propose a participatory research approach: the Digitalization–Sustainability Matrix (DSM). The DSM serves as a means for collaborative methods, such as participatory action research (PAR), for the knowledge production process. We exercised the DSM in the Digitainable Thinkathon event, a gathering of experts from diverse sectors and backgrounds for capturing the action-oriented dialogues concerning the use of D&AI technologies for the indicators of SDGs 4 (Education) and 13 (Climate Action). As a tool, the DSM aided in the discussion by systematically capturing transdisciplinary knowledge generated on several aspects, such as: (1) the need for research–practice nexus action; (2) data-capturing efforts and social considerations; (3) collaborative planning for utilizing the power of D&AI; (4) lessons from the diverse community to encourage the purposeful use of technologies. Overall, the proposed approach effectively triggered a discussion on the crucial aspects that need to be considered for D&AI's practices, a step towards deep-rooting the transdisciplinary perspectives for meaningful use of D&AI for SDGs. [ABSTRACT FROM AUTHOR]

Healthcare supply chain management (HCSCM) is a network of different systems, tools, and processes. This system works in a seamless manner to ensure healthcare related materials such as medicines, hospital supplies, and so on. These materials are manufactured in the firms, distributed through agents, and eventually reach patients on time. During COVID-19, HCSCM was severely impacted in many countries. Many patients had to face severe difficulties during the pandemic. But post pandemic and after the adoption of several modern technologies like Industry 4.0, the performance of HCSCM was more reliable. Not many studies are available which have examined the influence of Industry 4.0 in HCSCM during such a crisis. In such context, the study aims at examining the influence of Industry 4.0 in HCSCM with moderating role of environmental dynamism. A conceptual model is proposed with the support of theories and literature. This was later validated by PLS-SEM approach considering 312 respondents from people of HCSCM system. The study ascertained that Industry 4.0 has greatly improved the performance of HCSCM system after COVID-19 crisis. In addition, this study highlighted that environmental dynamism has a significant moderating role on the linkage between adoption of industry 4.0 technology with HCSCM. [ABSTRACT FROM AUTHOR]