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In contrast to earlier studies, the most recent studies on the incremental value relevance of earnings and cash flows from operations find that both earnings and cash flows have incremental value relevance beyond each other. An interesting question that follows is whether these findings hold after controlling the extremity of earnings and cash flows. This study, therefore, aims to examine the incremental value relevance of earnings and cash flows in the following four cases: moderate earnings and moderate cash flows, moderate earnings and extreme cash flows, extreme earnings and moderate cash flows and extreme earnings and extreme cash flows.

To evaluate the incremental value relevance (information content) of earnings and cash flows for each of the four cases mentioned above, we examine the statistical significance of the slope coefficients for regression of returns on both unexpected earnings and unexpected cash flows from operations.

The results show that (i) both moderate and extreme earnings have incremental value relevance beyond both moderate and extreme cash flows, (ii) moderate cash flows have incremental value relevance beyond both moderate and extreme earnings and (iii) extreme cash flows lack incremental value relevance beyond moderate earnings; however, they (extreme cash flows) have incremental value relevance beyond extreme earnings. These results suggest that earnings and cash flows have incremental value relevance. However, only in cases when cash flows are extreme and earnings are moderate, cash flows do not possess incremental value relevance. In further analysis, we find that the value relevance for cash flows and earnings decreases when they are extreme and transitory. Moreover, the value relevance for cash flows increases when they are moderate (not extreme) and the other competing measure (earnings) is transitory and extreme.

The results support the idea that earnings and cash flows from operations complement each other in explaining variation in returns. However, when cash flows are extreme and less informative, investors rely more on earnings in firm valuation, especially when earnings are moderate. Because earnings are unlikely to persist to be permanent across the years, these results can be interpreted as indicating that cash flows and earnings information are used jointly by investors.

In contrast to previous studies, we control for the extremity of earnings and cash flows when evaluating the incremental value relevance of earnings and cash flows from operations.

Many studies examine the relative information content of earnings and cash flows from operations. Most studies find that earnings have higher information content than cash flows. An interesting question that follows is whether these findings hold after controlling the extremity of earnings and cash flows. The purpose of this paper is to examine the relative information content of earnings and cash flows in the following four different cases: first, moderate earnings vs moderate cash flows, second, extreme earnings vs moderate cash flows, third, moderate earnings vs extreme cash flows, and fourth, extreme earnings vs extreme cash flows.

To assess the relative information content of earnings and cash flows for each of the four cases mentioned above, the authors compare the explanatory power for regression of returns on unexpected earnings relative to regression of returns on unexpected cash flows. Therefore, the author compares the adjusted R2 of the model with earnings variables and the model with cash flows variables using Vuong's test, that examines the statistical significance of the difference between adjusted R2s of the rival (non-nested) models, and interpret a statistically higher adjusted R2 as an indicator for higher relative information content.

The results show that: first, when both earnings and cash flows are moderate, earnings are more highly associated with stock market price changes than cash flows, second, when both earnings and cash flows are extreme, earnings also have greater relative information content than cash flows, third, when the extremity differs between earnings and cash flows, the moderate variable is superior to the other extreme variable in explaining security returns. These results suggest that earnings are definitely more value relevant than cash flows. However, only in cases when cash flows from operations are moderate and earnings are extreme, cash flows possess higher information content than earnings.

The explanatory power for stock returns will be higher for earnings or cash flows depending on which is more highly persistent. This result reverses the conventional finding of the superiority of earnings over cash flows in explaining security returns.

In contrast to previous studies, the authors control for the extremity of earnings and cash flows when evaluating the relative information content of earnings and cash flows from operations.

Extensive efforts have been conducted on the real-time strategy (RTS) games. The purpose of this paper is the specific artificial intelligence (AI) challenges posed by RTS games; non-player character (NPC) is started out by collecting game-map resources to build up defenses and attack forces, to upgrade combat deployment.

The authors used weak AI fuzzy theory as the foundation for tunable development. With the fuzzy theory, the AI was more humanistic in its judgment process.

Well-developed AIs have been used brilliantly in various aspects in RTS games, especially in those developed by large production teams. For small production teams, how to develop an AI system in less time and at a lower cost is extremely important.

This study aimed to develop a system using player unit threat levels for NPC deployment and arrangement so that the further strategy would be adopted for NPCs in response to player actions.

The RTS games would become more challenging for players to play.

This paper aims to investigate the moderating effects of the role a plant plays in a manufacturing network on the relationships between its level of integration with other plants in the same manufacturing network (referred as “internal manufacturing network integration” in this paper), its interactions with suppliers/customers (referred as “external supply chain integration” in this paper), and its operational performance.

This paper is based on the data from the sixth version of International Manufacturing Strategy Survey (IMSS VI). Specifically, this paper uses a subset of the IMSS VI data set from the 606 plants that identified themselves as one of the plants in a manufacturing network.

The results demonstrate plant role has a moderating effect on the relationship between internal manufacturing network integration and external supply chain integration, but not on the relationship between external supply chain integration and operational performance. Our research also highlights that plant role moderates the mediating effect of external supply chain integration on the relationship between internal manufacturing network integration and operational performance. More importantly, it indicates that if a plant wants to significantly increase its operational performance, it will have to strengthen its linkages with supply chain partners, no matter which role it plays in manufacturing network.

This paper supplements the existing research by developing further understandings on the relationship between internal manufacturing network integration, external supply chain integration and operational performance. Specifically, it examines the influence of plant role on such relationship and reveals the essence about in what context (in terms of plant characteristics) internal manufacturing network integration influences the operational performance of a plant in a manufacturing network through external supply chain integration.

This study aims to explore the role of artificial intelligence (AI)-powered chatbot marketing efforts (CMEs) in the establishment of relationships between brands and their customers, extending the link between relationship marketing and online consumer behavioral intentions.

Data are collected from 1,072 customers in the USA, who used chatbot marketing activities from any of 30 brands leading their industries in messaging innovation. Structural equation modeling is used for data analysis.

Results show that interaction, information, accessibility, entertainment and customization are important CMEs components. CMEs have significant direct effects on the quality of communication with chatbot agents and indirectly affect customer–brand relationships (CBR) and customer response. In addition, the findings demonstrate that CBR mediates the association between communication quality and customer response.

Implications of this study can enable practitioners to understand the effects of AI on user experiences and provide a guide for the development of CMEs strategies and relationship building.

This study focused on the impact of misinformation on social networking sites. Through theorizing and integrating literature from interdisciplinary fields such as information behavior, communication and relationship management, this study explored how misinformation on Facebook influences users' trust, distrust and intensity of Facebook use.

This study employed quantitative survey research and collected panel data via an online professional survey platform. A total of 661 participants in the USA completed this study, and structural equation modeling (SEM) was used to test the theoretical model using Amos 20.

Based on data from an online questionnaire (N = 661) in the USA, results showed that information trustworthiness and elaboration, users' self-efficacy of detecting misinformation and prescriptive expectancy of the social media platform significantly predicted both trust and distrust toward Facebook, which in turn jointly influenced users' intensity of using this information system.

This study contributes to the growing body of literature on information and relationship management and digital communication from several important aspects. First, this study disclosed the underlying cognitive psychological and social processing of online misinformation and addressed the strategies for future system design and behavioral intervention of misinformation. Second, this study systematically examined both trust and distrust as cognitive and affective dimensions of the human mindsets, encompassed the different components of the online information behavior and enriched one’s understanding of how misinformation affected publics' perceptions of the information system where it appeared. Last but not least, this study advanced the relationship management literature and demonstrated that a trustful attitude exerted a stronger influence on the intensity of Facebook use than distrust did.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2020-0130

Informed by the third-person effects (TPE) theory, this study aims to analyze restrictive versus corrective actions in response to the perceived TPE of misinformation on social media in the USA.

The authors conducted an online survey among 1,793 adults in the USA in early April. All participants were randomly enrolled in this research through a professional survey company. The structural equation modeling via Amos 20 was adopted for hypothesis testing.

Results indicated that individuals also perceived that others were more influenced by misinformation about COVID-19 than they were. Further, such a perceptual gap was associated with public support for governmental restrictions and corrective action. Negative affections toward health misinformation directly affected public support for governmental restrictions rather than corrective action. Support for governmental restrictions could further facilitate corrective action.

This study examined the applicability of TPE theory in the context of digital health misinformation during a unique global crisis. It explored the significant role of negative affections in influencing restrictive and corrective actions. Practically, this study offered implications for information and communication educators and practitioners.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2020-0386

The purpose of this paper is to investigate the relationships at the level of plant in a manufacturing network, labelled as networked plant in the paper, between inter-plant coordination and operational performance, supply chain integration (SCI) and operational performance and inter-plant coordination and SCI.

This paper is developed based on the data obtained from the sixth version of International Manufacturing Strategy Survey (IMSS VI). Specifically, this paper uses a subset of the IMSS VI data set from the 606 plants that identified themselves as one of the plants in a manufacturing network.

This paper finds that external integration is significantly related to operational performance of networked plant, whereas internal integration is not. As an enabler for external integration, the influence of internal integration on operational performance of networked plant is mediated by external integration. This paper also provides evidence to the purported positive impact of internal integration on inter-plant coordination, as well as the positive impact of inter-plant coordination on external integration. It further suggests that inter-plant coordination can influence operational performance of networked plant through external integration and also mediate the relationship from internal integration to performance through external integration.

This paper contributes to the SCI literature and extends the understanding of the impact of SCI on the operational performance by selecting networked plant as a unit of analysis. Besides, this paper distinguishes inter-plant coordination from SCI and investigates the relationship between inter-plant coordination, SCI, and operational performance for the first time.

The purpose of this paper is to find the driving factors for effective and efficient management of international manufacturing network (IMN) which has become increasingly important due to the intensive competition existing in the manufacturing industry. This paper presents a magnified view of different constructs of IMN and identifies the qualitative factors which are broadly classified under different constructs like political, economic, social, technological, legal and environmental.

Principal component analysis is applied to club identified factors into political, economic, social, technological and legal categories. PESTLE–SWOT approach is used to shortlist the identified factors using the elimination algorithm. Using analytical hierarchy process, weightages and rank of the identified factors are evaluated. Interpretive structural modeling is applied to understand inter-relationship among factors, and to analyze the driving power and dependence of the factors.

The results obtained from the above approaches are compared to identify most significant factors and the list of IMN enablers is presented using Venn Diagram. Government stability, Economic freedom, economic stability, environmental sustainability and raw material availability are coming out to be the driving factors across all techniques. Finally, the paper provides weights of the driving indicators common across all multi-criteria decision-making techniques considered.

The proposed work provides significant information about the qualitative factors needed to be considered while designing and developing IMN. As identified by literature review and through expert opinions, not all 29 factors responsible for IMN development are crucial. Many factors are either interdependent or driven by others. The paper provides a useful analysis about the driving factors that need to be taken into account.

The study presents a comprehensive analysis of the IMN enablers. Furthermore, it provides managerial and theoretical implications to deal with the complexities encountered while establishing IMN.

Drawing on theoretical insights from the persuasion knowledge model (PKM), this study aims to propose and test a model that maps out the antecedents, process and consequences to explain how consumers process and respond to fake news about brands on Facebook.

Contextualizing the fake news about Coca-Cola’s recall of Dasani water, an online survey was conducted via Qualtrics with consumers in the USA (N  =  468). Data were analyzed using covariance-based structural equation modeling.

Results showed that self-efficacy and media trust significantly predicted consumers’ persuasion knowledge of the fake news. Persuasion knowledge of the fake news significantly influenced consumers’ perceived diagnosticity of the fake news and subsequent brand trust. Furthermore, persuasion knowledge of the fake news mediated the effects from self-efficacy on perceived diagnosticity of the fake news and brand trust, respectively.

This study contributes to the literature of brand management by examining how consumers process and respond to fake news about a brand. It also extends the persuasion knowledge model by applying it to the context of fake news about brands on social media, and incorporating antecedents (self-efficacy and media trust) and consequences (perceived diagnosticity and brand trust) of persuasion knowledge in this particular context. Practically, this study provides insights to key stakeholders of brands to better understand consumers’ information processing of fake news about brands on social media.