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This paper aims to propose a framework on complaint management system for quality management by applying the text mining method and potential failure identification that can support organization learning (OL). Customer complaints in the form of email text is the input of the framework, while the most frequent complaints are visualized using a Pareto diagram. The company can learn from this Pareto diagram and take action to improve their process.

The first main part of the framework is creating a defect database from potential failure identification, which is the initial part of the failure mode and effect analysis technique. The second main part is the text mining of customer email complaints. The last part of the framework is matching the result of text mining with the defect database and presenting in the form of a Pareto diagram. After the framework is proposed, a case study is conducted to illustrate the applicability of the proposed method.

By using the defect database, the framework can interpret the customer email complaints into the list of most defect complained by customer using a Pareto diagram. The results of the Pareto diagram, based on the results of text mining of consumer complaints via email, can be used by a company to learn from complaint and to analyze the potential failure mode. This analysis helps company to take anticipatory action for avoiding potential failure mode happening in the future.

The framework on complaint management system for quality management by applying the text mining method and potential failure identification is proposed for the first time in this paper.

This paper tries to generalize business process improvement (BPI) methodology. It utilizes the seven-waste framework as an essential step in the methodology. While the seven-waste concept is usually applied for manufacturing activities, this paper tries to explore the applicability of it to office-work activities. Also, this paper demonstrates that information technology can be used as a tool for reducing waste in the office-work.

A comprehensive literature review of BPI methodology studies was conducted in order to propose systematic flowcharts to represent the sequence of processes involved in each step of BPI methodology. The proposed flowcharts are applied to a case study in supply chain planning and allocation planning at a manufacturing company. The seven-waste framework is designed as part of the step, in which equivalency between the definition of waste found on the production floor and waste found in office work is presented.

The BPI methodology generally follows five steps: initialization, selection, design, implementation and evaluation. The seven-waste framework is effectively applied in the selection step. The case study shows that information technology can be used as a tool in business process improvement to reduce waste in the business process.

The case study indicates that the proposed framework and methodology are proven able to reduce the three key performance indicators. They are the number of steps from 54 to 24 (55% reduction), processing time from 890 min to 313.5 min (64% reduction) and the number of the manual process from 41 to 17 (59% reduction).

This paper proposes a generalization of BPI methodology, the seven-waste framework in the selection step of the BPI methodology, the seven-waste concept in office-work activity and the use of information technology for BPI by reducing waste in office-work activity.

This paper describes and discusses collaboration between history teachers in England and Japan. The purpose of this paper is to explore the ways in which history is taught in each country as a part of a general commitment to international collaboration and as a means by which we could explore the connection between history education and global citizenship education.

The teachers created two lessons (one from England and one from Japan) about the Russian revolution. Both lessons were taught in each country. Data were gathered from students and teachers to aid reflections on the nature and outcome of the collaboration.

The collaboration was very positive. Teachers and students were excited to work together and to experience different ways of learning about the past. There were different approaches to the ways in which knowledge was characterized in each country (teachers in England emphasizing contextually based historical interpretations; teachers in Japan emphasizing content and contextual knowledge).

This work contributes to the limited amount of research that is currently available about professional collaboration between high school teachers and students of history in Japan and England. The arguments that are made about the opportunities for international collaboration in the context of different characterizations of pedagogical content knowledge contribute to a relatively unexplored field. The authors contribute to our understandings of the relationship between history education and global citizenship education.

Although uncertainty has been identified as a key crisis characteristic and a multi-faceted construct essential to effective crisis management research and practice, only a few studies examined publics' perceived uncertainty with a focus on crisis severity uncertainty, leaving crisis responsibility uncertainty uninvestigated in organizational crisis settings.

To close this research gap empirically, this study employed data from an online survey of a total of 817 US adults to examine how participants' crisis responsibility uncertainty and their attribution-based crisis emotions might impact their crisis responses such as further crisis information seeking.

First, findings show that participants' crisis responsibility uncertainty was negatively associated with their attribution-independent (AI) crisis emotions (i.e. anxiety, fear, apprehension and sympathy) and external-attribution-dependent (EAD) crisis emotions (i.e. disgust, contempt, anger and sadness), but positively associated with internal-attribution-dependent (IAD) crisis emotions (i.e. guilt, embarrassment and shame). Second, crisis responsibility uncertainty and AI crisis emotions were positive predictors for participants' further crisis information seeking. Third, AI crisis emotions and IAD crisis emotions were parallel mediators for the relationship between participants' crisis responsibility uncertainty and their further crisis information seeking.

Organizations need to pay attention to the perceived uncertainty about crisis responsibility and attribution-based crisis emotions since they can impact the decision of seeking crisis information during an ongoing organizational crisis.

This study improves uncertainty management in organizational crisis communication research and practice, connecting crisis responsibility uncertainty, attribution-based crisis emotions and publics' crisis information seeking.

Disinformation, false information designed with the intention to mislead, can significantly damage organizational operation and reputation, interfering with communication and relationship management in a wide breadth of risk and crisis contexts. Modern digital platforms and emerging technologies, including artificial intelligence (AI), introduce novel risks in crisis management (Guthrie and Rich, 2022). Disinformation literature in security and computer science has assessed how previously introduced technologies have affected disinformation, demanding a systematic and coordinated approach for sustainable counter-disinformation efforts. However, there is a lack of theory-driven, evidence-based research and practice in public relations that advises how organizations can effectively and proactively manage risks and crises driven by AI (Guthrie and Rich, 2022).

As a first step in closing this research-practice gap, the authors first synthesize theoretical and technical literature characterizing the effects of AI on disinformation. Upon this review, the authors propose a conceptual framework for disinformation response in the corporate sector that assesses (1) technologies affecting disinformation attacks and counterattacks and (2) how organizations can proactively prepare and equip communication teams to better protect businesses and stakeholders.

This research illustrates that future disinformation response efforts will not be able to rely solely on detection strategies, as AI-created content quality becomes more and more convincing (and ultimately, indistinguishable), and that future disinformation management efforts will need to rely on content influence rather than volume (due to emerging capabilities for automated production of disinformation). Built upon these fundamental, literature-driven characteristics, the framework provides organizations actor-level and content-level perspectives for influence and discusses their implications for disinformation management.

This research provides a theoretical basis and practitioner insights by anticipating how AI technologies will impact corporate disinformation attacks and outlining how companies can respond. The proposed framework provides a theory-driven, practical approach for effective, proactive disinformation management systems with the capacity and agility to detect risks and mitigate crises driven by evolving AI technologies. Together, this framework and the discussed strategies offer great value to forward-looking disinformation management efforts. Subsequent research can build upon this framework as AI technologies are deployed in disinformation campaigns, and practitioners can leverage this framework in the development of counter-disinformation efforts.

This paper aims to examine the relationships between anthropomorphic cues (i.e. degrees of the humanized profile picture and naming) in artificial intelligence (AI) chatbots and business types (utilitarian-centered business vs hedonic-centered business) on consumers’ attitudes toward the AI chatbot and intentions to use the AI chatbot app and to accept the AI chatbot’s recommendation.

An online experiment with a 2 (humanized profile pictures: low [semihumanoid] vs high [full-humanoid]) × 2 (naming: Mary vs virtual assistant) × 2 (business types: utilitarian-centered business [bank] vs hedonic-centered business [café]) between-subjects design (N = 520 Mturk samples) was used.

The results of this study show significant main effects of anthropomorphic cues (i.e. degrees of profile picture and naming) in AI chatbots and three-way interactions among humanized profile pictures, naming and business types (utilitarian-centered business vs hedonic-centered business) on consumers’ attitudes toward the AI chatbot, intentions to use the AI chatbot app and intentions to accept the AI chatbot’s recommendation. This indicates that the high level of anthropomorphism generates more positive attitudes toward the AI chatbot and intentions to use the AI chatbot app and to accept the AI chatbot’s recommendation in the hedonic-centered business condition. Moreover, the mediated role of parasocial interaction occurs in this relationship.

This study is the original endeavor to examine the moderating role of business types influencing the effect of anthropomorphism on consumers’ responses, while existing literature overweighted the value of anthropomorphism in AI chatbots without considering the variation of businesses.

The increasing awareness and adoption of technology, particularly artificial intelligence (AI), reshapes industries and daily life, fostering a proactive approach to risk management and leveraging advanced analytics, which may affect the stock price crash risk (SPCR). The main objective of the current study is to explore how AI adoption influences SPCR.

This study employs an Ordinary Least Squares (OLS) fixed-effect regression model to explore the impact of AI on SPCR in Chinese A-share listed companies from 2010 to 2020. Further, number of robustness analysis (2SLS, PSM and Sys-GMM) and channel analysis are used to validate the findings.

The primary findings emphasize that AI adoption significantly reduces SPCR likelihood. Further, channel analysis indicates that AI adoption enhances internal control quality, contributing to a reduction in firm SPCR. Additionally, the observed relationship is notably more pronounced in non-state-owned enterprises (non-SOEs) compared to state-owned enterprises (SOEs). Similarly, this distinction is heightened in nonforeign enterprises (non-FEs) as opposed to foreign enterprises (FEs). The study finding also supports the notion that financial analysts enhance transparency, reducing the SPCR. Moreover, the study results consistently align across different statistical methodologies, including 2SLS, PSM and Sys-GMM, employed to effectively address endogeneity concerns.

Our study stands out for its distinctive focus on the financial implications of AI adoption, particularly how it influences firm-level SPCR, an area that has been overlooked in previous research. Through the lens of information asymmetry theory, agency theory, and the economic implications of integrating AI into financial markets, our study makes a substantial contribution in mitigating SPCR.

This study underscores the pivotal role of AI adoption in influencing stock markets for enterprises in China. Embracing digital strategies, fostering transparency and prioritizing talent development are key for reaping substantial benefits. The study recommends regulatory bodies and service providers to promote AI adoption in strengthening financial supervision and ensure market stability, emphasizing the importance of investing in technologies and advancing talent development.

Artificial intelligence (AI) is revolutionizing product recommendations, but little is known about consumer acceptance of AI recommendations. This study examines how to improve consumers' acceptance of AI recommendations from the perspective of product type (material vs experiential).

Four studies, including a field experiment and three online experiments, tested how consumers' preference for AI-based (vs human) recommendations differs between material and experiential product purchases.

Results show that people perceive AI recommendations as more competent than human recommendations for material products, whereas they believe human recommendations are more competent than AI recommendations for experiential products. Therefore, people are more (less) likely to choose AI recommendations when buying material (vs experiential) products. However, this effect is eliminated when is used as an assistant to rather than a replacement for a human recommendation.

This study is the first to focus on how products' material and experiential attributes influence people's attitudes toward AI recommendations. The authors also identify under what circumstances resistance to algorithmic advice is attenuated. These findings contribute to the research on the psychology of artificial intelligence and on human–technology interaction by investigating how experiential and material attributes influence preference for or resistance to AI recommenders.

Artificial intelligence (AI) is transforming consumers' experiences and how firms identify, create, nurture and manage interactive marketing relationships. However, most marketers do not have a clear understanding of what AI is and how it may mutually benefit consumers and firms. In this paper, the authors conduct an extensive review of the marketing literature, develop an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships, identify research gaps and offer a future research agenda.

The authors first conduct an extensive literature review in 16 top marketing journals on AI. Based on this review, an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships was conceptualized.

The literature review led to a number of key research findings and summary areas: (1) an historical perspective, (2) definitions and boundaries of AI, (3) AI and interactive marketing, (4) relevant theories in the domain of interactive marketing and (5) synthesizing AI research based on antecedents to AI usage, interactive AI usage contexts and AI-enabled value co-creation outcomes.

This is one of the most extensive reviews of AI literature in marketing, including an evaluation of in excess or 300 conceptual and empirical research. Based on the findings, the authors offer a future research agenda, including a visual titled “What is AI in Interactive Marketing? AI design factors, AI core elements & interactive marketing AI usage contexts.”

This study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates technical and macroeconomic indicators.

In this study we used advance machine learning techniques, such as gradient boosting regression (GBR), random forest (RF) and notably long short-term memory (LSTM) networks, this research provides a nuanced understanding of the factors driving the performance of AI tokens. The study’s comparative analysis highlights the superior predictive capabilities of LSTM models, as evidenced by their performance across various AI digital tokens such as AGIX-singularity-NET, Cortex and numeraire NMR.

This study finding shows that through an intricate exploration of feature importance and the impact of speculative behaviour, the research elucidates the long-term patterns and resilience of AI-based tokens against economic shifts. The SHapley Additive exPlanations (SHAP) analysis results show that technical and some macroeconomic factors play a dominant role in price production. It also examines the potential of these models for strategic investment and hedging, underscoring their relevance in an increasingly digital economy.

According to our knowledge, the absence of AI research frameworks for forecasting and modelling current aria-leading AI tokens is apparent. Due to a lack of study on understanding the relationship between the AI token market and other factors, forecasting is outstandingly demanding. This study provides a robust predictive framework to accurately identify the changing trends of AI tokens within a multivariate context and fill the gaps in existing research. We can investigate detailed predictive analytics with the help of modern AI algorithms and correct model interpretation to elaborate on the behaviour patterns of developing decentralised digital AI-based token prices.