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This study aims to investigate how two different types of brand-to-brand dialogues – “roasting” versus “toasting” – impact consumers’ brand perceptions, particularly perceived entertainment, and influence brand attitudes.

The research design comprises four studies. The preliminary study involves Web scraping to gauge consumer perception about the two communication approaches followed by two well-known brands. Study 1 involves an online experiment to compare these communication types within each brand tested in the pilot study and examines the mediation effect of perceived entertainment. Study 2, also an online experiment, investigates the role of message neutralization, demonstrating that “roasting” can be acceptable when the humor is neutralized. Study 3 further tests the effects of neutralized “roasting” at different levels of brand familiarity and personality.

Roasting can lead to more favorable consumer perceptions than toasting. The effect can be explained by roasting’s higher level of perceived entertainment. However, this positive outcome is contingent on the successful neutralization of the aggressive humor in the “roasting” messages. When it comes to brand familiarity and personality, familiar brands benefit more from neutralized “roasting,” whereas brand personality does not have a strong influence.

The findings suggest that “roasting” can be effective when messages are neutralized, and “toasting” works best when spontaneous and genuine. It highlights how brand familiarity and personality influence consumer reactions, thus, offering strategic insights for both established and lesser-known brands. The study also prompts further research to examine other brand traits, cultural factors and behavioral dimensions in brand-to-brand dialogue, signifying the complexity and richness of this growing research area.

This study advises lesser-known brands to adopt “toasting” strategies to build a positive image, while established brands can try “roasting,” ensuring message neutrality to avoid negativity. The research emphasizes the role of brand familiarity and personality in shaping brand dialogues. Marketers must consider these to make humor strategies effective and bolster positive brand image.

This research uniquely examines message neutralization through contextual cues as a strategy brands can use to aid their sensitive dialogues with others on social media. The findings provide new insights into how brands can use different types of messages in digital communications to attract consumers and ensure positive reception, offering valuable guidance for academics and practitioners in brand-to-brand dialogue.

Tacit knowledge in frontline operations is primarily reflected in the holders’ intuition about dynamic systems. Despite the implicit nature of tacit knowledge, the understanding of complex systems it encapsulates can be displayed through formalization methods. This study seeks to develop a methodology for formalizing tacit knowledge in a dynamic delivery system.

This study employs a structured survey to gather experiential knowledge from dispatchers engaged in last-mile delivery operations. This knowledge is then formalized using a value function approximation approach, which transforms tacit insights into structured inputs for dynamic decision-making. We apply this methodology to optimize delivery operations in an online-to-offline pharmacy context.

The raw system feature data are not strongly correlated with the system’s development trends, making them ineffective for guiding dynamic decision-making. However, the system features obtained through preprocessing the raw data increase the predictiveness of dynamic decisions and improve the overall effectiveness of decision-making in delivery operations.

This research provides a foundational framework for studying sequential dynamic decision problems, highlighting the potential for improved decision quality and system optimization through the formalization and integration of tacit knowledge.

This approach proposed in this study offers a method to preserve and formalize critical operational expertise. By embedding tacit knowledge into decision-making systems, organizations can enhance real-time responsiveness and reduce operational costs.

This study presents a novel approach to integrating tacit knowledge into dynamic decision-making frameworks, demonstrated in a real-world last-mile delivery context. Unlike previous research that focuses primarily on explicit data-driven methods, our approach leverages the implicit, experience-based insights of operational staff, leading to more informed and effective decision-making strategies.

This study aims to investigate how and when frontline employee (FLE) diversity influences brand equity in a luxury fashion brand setting.

Three experiments test the framework. The first experiment investigates the direct effect of FLE diversity on brand equity, the second explores the mediating mechanism (brand rebelliousness and brand coolness) and the third examines material values as the moderator of these effects.

Results show that FLE diversity increases luxury fashion brand equity. A serial mediation mechanism explains this effect: FLE diversity drives perceptions of brand rebelliousness, which in turn increases brand coolness and consequently brand equity. Finally, results show that, for consumers high in material values, the effect of brand rebelliousness on brand coolness is weaker.

This paper identifies a blind spot in luxury management diversity practices: FLEs. Findings highlight an effective strategy for luxury brands to enhance their brand equity and contribute to a deeper understanding of a dynamic consumer environment.

Findings suggest that luxury fashion brands must urgently improve their diversity efforts, as consumers value FLE diversity and evaluate such brands more favourably. The findings are particularly relevant to brands aiming to target modern consumers, who place greater value on diversity and social responsibility.

This study bridges the gap between management and marketing studies on diversity, uncovering a previously overlooked link between FLE diversity and brand equity. Furthermore, this work acknowledges the importance of intersectionality and concurrently tests multiple dimensions of diversity on brand equity.

Heterogeneous graphs, composed of diverse nodes and edges, are prevalent in real-world applications and effectively model complex web-based relational networks, such as social media, e-commerce and knowledge graphs. As a crucial data source in heterogeneous networks, Node attribute information plays a vital role in Web data mining. Analyzing and leveraging node attributes is essential in heterogeneous network representation learning. In this context, this paper aims to propose a novel attribute-aware heterogeneous information network representation learning algorithm, AAHIN, which incorporates two key strategies: an attribute information coverage-aware random walk strategy and a node-influence-based attribute aggregation strategy.

First, the transition probability of the next node is determined by comparing the attribute similarity between historical nodes and prewalk nodes in a random walk, and nodes with dissimilar attributes are selected to increase the information coverage of different attributes. Then, the representation is enhanced by aggregating the attribute information of different types of high-order neighbors. Additionally, the neighbor attribute information is aggregated by emphasizing the varying influence of each neighbor node.

This paper conducted comprehensive experiments on three real heterogeneous attribute networks, highlighting the superior performance of the AAHIN model over other baseline methods.

This paper proposes an attribute-aware random walk strategy to enhance attribute coverage and walk randomness, improving the quality of walk sequences. A node-influence-based attribute aggregation method is introduced, aggregating neighboring node attributes while preserving the information from different types of high-order neighbors.

This study aims to focuses on the paradox of the great resignation (GR), great layoff (GL) and moonlighting (ML) phenomena triggered by the unprecedented complexity, extreme emotional distress and uncertainty caused by the pandemic to explore ways for mitigating their impact on the intention to stay (ITS).

A systematic literature review was conducted to explore the impact of the paradoxical GR, layoffs and ML on the ITS by the text analysis of a pool of 111 published articles across 57 journals between 2012 and 2024 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach. The factors impacting the ITS, and the underlying interlinkages of the paradoxical GR, GL and ML phenomena were subsequently inductively deduced using Nvivo software.

The new theoretical framework explains the evolving dynamics of the ITS and prompts toward a concurrent approach of viewing the paradoxical GR, layoffs and ML through a “common lens” revealing novel insights.

Employees have reprioritized work-life balance, mental health, multiple jobs for varied income streams, flexible work schedules and job satisfaction. Human resource managers should prioritize these aspects and adapt to the evolving workforce dynamics to create a resilient, employee-centric organizational environment where employees choose to stay.

To the best of the authors’ knowledge, this is the first study to concurrently examine the current paradox of the GR, layoffs and ML for enhancing the ITS, necessitating a re-evaluation of traditional perceptions of unemployment and job seeking.