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This study aims to explore how ephemeral content marketing enhances brand love and customer engagement, with a focus on the mediating role of brand authenticity, self-brand connection and advertising value.

This research was conducted using a quantitative method through an online questionnaire with a sample of 728 in Vietnam, analyzing data by using the partial least squares structural equation modeling model.

This study evaluates ephemeral content marketing through six dimensions: entertainment, trendiness, informativeness, interactivity, aesthetic quality and perceived relevance. The findings indicate positive mediating roles of advertising value, self-brand connection and brand authenticity on the impact of ephemeral content marketing on brand love and customer engagement.

This study provides a comprehensive model of factors affecting consumer perceptions of ephemeral content marketing, which can help businesses to proactively formulate strategic responses for consumers on social media platforms with ephemeral content features. This also allows them to precisely target their audience, avoiding ineffective and costly advertising efforts on social media when content quality is lacking.

This research sheds light on the six essential dimensions of effective ephemeral content that adds value to customers, ultimately leading to their love and active engagement. This substantial addition to the field of social media marketing opens up possibilities for further investigation of the dynamics across different forms of social media marketing, such as short-form videos or in various contexts such as tourism, fashion, food products and education, particularly in the context of ephemeral content in emerging markets such as Vietnam.

In spite of the merits of artificial intelligence (AI) in marketing and social media, harm to consumers has prompted calls for AI auditing/certification. Understanding consumers’ approval of AI certification entities is vital for its effectiveness and companies’ choice of certification. This study aims to generate important insights into the consumer perspective of AI certifications and stimulate future research.

A literature and status-quo-driven search of the AI certification landscape identifies entities and related concepts. This study empirically explores consumer approval of the most discussed entities in four AI decision domains using an online experiment and outline a research agenda for AI certification in marketing/social media.

Trust in AI certification is complex. The empirical findings show that consumers seem to approve more of non-profit entities than for-profit entities, with the government approving the most.

The introduction of AI certification to marketing/social media contributes to work on consumer trust and AI acceptance and structures AI certification research from outside marketing to facilitate future research on AI certification for marketing/social media scholars.

For businesses, the authors provide a first insight into consumer preferences for AI-certifying entities, guiding the choice of which entity to use. For policymakers, this work guides their ongoing discussion on “who should certify AI” from a consumer perspective.

To the best of the authors’ knowledge, this work is the first to introduce the topic of AI certification to the marketing/social media literature, provide a novel guideline to scholars and offer the first set of empirical studies examining consumer approval of AI certifications.

This study serves a dual purpose. First, it aims to explore the phase-wise progression that small and medium-sized enterprises (SMEs) and startups must undertake to become successful ecosystem partners, supporting large industrial firms in their circular transition. Second, it seeks to examine how these small firms manage change and foster collaborative cultures through strategies enabled by positive organizational scholarship (POS) during their phased evolution.

This study provides empirical evidence through a multiple case study-based approach involving 12 born-circular SMEs/startups from 5 diverse Indian industrial sectors. Insights were gathered by conducting two rounds of semi-structured interviews with 24 participants and one validatory seminar with eight participants.

This research identified three distinct and complementary phases – compare, compete and collaborate – that SMEs/startups can undergo to emerge as successful ecosystem partners. Each phase encompasses specific business practices, including various circular activities. These activities serve as clear indicators of the smaller firms’ potential competence in aiding larger firms during their circular transitions.

This paper contributes to the theoretical understanding of the circular economy by outlining a trajectory for SMEs/startups to establish successful partnerships. Another contribution is the application of POS as a positive change management paradigm to facilitate circularity. Additionally, the study highlights the context of developing nations, which remain underexplored compared to their developed counterparts in circularity initiatives.

This study investigates the evolution of digital transformation research trends in the context of the fashion industry. The paper analyzes and addresses the impact of digital innovations on three areas of the industry: products, processes and business models.

The study examined 277 peer-reviewed articles using various bibliometric tools and indicators, aiming to identify and organize the contributions of the most influential works. Additionally, a critical review of the ten most cited papers in the field was carried out.

The study shows that digitalization is not merely a technological trend; rather, it is a transformative force reshaping the fashion industry. It fosters collaboration, innovation and sustainability, creating an ecosystem where businesses can thrive by aligning with circular principles and leveraging digital tools.

The paper reveals a gap in the scientific systematization and exploration of the variety of applications of digital tools in the fashion industry. The study’s limitations include the keywords chosen for the research and the period of the research from 1998 to 2023.

The paper pursues to contribute to the current discussion on sustainable practices within the fashion industry, while also setting the foundation for future progress in digital innovation that supports the increasing need for sustainable and personalized products.

In this study, the authors examine artificial knowledge as a fundamental stream of knowledge management for sustainable and resilient business models in supply chain management (SCM). The study aims to provide a comprehensive overview of artificial knowledge and digitalization as key enablers of the improvement of SCM accountability and sustainable performance towards the UN 2030 Agenda.

Using the SCOPUS database and Google Scholar, the authors analyzed 135 English-language publications from 1990 to 2022 to chart the pattern of knowledge production and dissemination in the literature. The data were collected, reviewed and peer-reviewed before conducting bibliometric analysis and a systematic literature review to support future research agenda.

The results highlight that artificial knowledge and digitalization are linked to the UN 2030 Agenda. The analysis further identifies the main issues in achieving sustainable and resilient SCM business models. Based on the results, the authors develop a conceptual framework for artificial knowledge and digitalization in SCM to increase accountability and sustainable performance, especially in times of sudden crises when business resilience is imperative.

The study results add to the extant literature by examining artificial knowledge and digitalization from the resilience theory perspective. The authors suggest that different strategic perspectives significantly promote resilience for SCM digitization and sustainable development. Notably, fostering diverse peer exchange relationships can help stimulate peer knowledge and act as a palliative mechanism that builds digital knowledge to strengthen and drive future possibilities.

This research offers valuable guidance to supply chain practitioners, managers and policymakers in re-thinking, re-formulating and re-shaping organizational processes to meet the UN 2030 Agenda, mainly by introducing artificial knowledge in digital transformation training and education programs. In doing so, firms should focus not simply on digital transformation but also on cultural transformation to enhance SCM accountability and sustainable performance in resilient business models.

This study is, to the authors' best knowledge, among the first to conceptualize artificial knowledge and digitalization issues in SCM. It further integrates resilience theory with institutional theory, legitimacy theory and stakeholder theory as the theoretical foundations of artificial knowledge in SCM, based on firms' responsibility to fulfill the sustainable development goals under the UN's 2030 Agenda.

The transition from fossil fuel-based energy systems to renewable energy sources, commonly referred to as the energy transition, is essential for combating climate change. However, comprehensive studies that thoroughly examine the financial mechanisms involved in this process are lacking. Despite the availability of various financial tools, there is a notable absence of extensive research that synthesizes and categorizes these mechanisms into broad groups.

A systematic literature review is used to explore a comprehensive framework for financial mechanisms related to the energy transition and their application across six stages of the process.

The framework of financial mechanisms for energy transition encompasses these six factors: public financing mechanisms, private financing mechanisms, market-based mechanisms, innovative financing mechanisms, risk mitigation instruments and institutional support and capacity building.

This is the first study that thoroughly reviewed the financial mechanisms involved in the energy transition process.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

The rise of the coronavirus disease 2019 (COVID-19) pandemic has enabled researchers and industry professionals to reinvent their strategies for basic economic understanding. Two years after the outbreak of the pandemic, businesses are now trying to adapt to the impact it has brought, hoping to receive support as it did in the past. However, before this feat can be accomplished, it is imperative to understand the recovery hurdles created by the pandemic. This research aims to fill the literature gaps by examining the challenges during recovery within the creative small and medium-sized enterprise (SME) industry, as there are few relevant studies that focus on this field.

Through a methodical bibliometric literature review and network analysis, the paper intends to critically explore relevant recovery challenges within the field while providing answers to the appropriate research questions. A total of 43 articles were selected for an in-depth review. Using the analysis from the selected articles as a guide, a framework was developed to address the recovery challenges alongside the recommended propositions.

The findings from this paper suggest that a lack of synergy among four major categories (governmental, supply chain, organizational and stakeholders) contributes to recovery challenges within the field of research.

The review also offers clarification in understanding the current and upcoming trends within the creative industry, SMEs and COVID-19. This paper can thus help researchers, industry practitioners and managers discover and analyze the recovery challenges brought about by the COVID-19 pandemic.

This study aims to investigate the evolving concept of the metaverse and its implications for service innovation. It seeks to understand how integrating technologies such as extended reality, blockchain, artificial intelligence and non-fungible tokens enables companies to experiment and innovate.

Adopting a qualitative methodology, this investigation conducts an immersive netnography across more than 25 case studies spanning diverse industries such as gaming, retail, health care and education. The thematic analysis method is used to distill critical insights, providing a deep dive into the technological enablers, innovation processes and market adaptations within the metaverse.

The study addresses four main building blocks through which companies experiment with the metaverse to foster innovation: enabling virtual identities’ agency, developing non-fungible tokens, designing immersive paths and crafting phygital microworlds. They shape the metaverse by enacting actors, resources, processes and phygital ecosystems. Companies obtain learning outcomes from such experimentation and identify learning challenges.

This research contributes to the nascent body of knowledge on the metaverse and service innovation by providing a comprehensive framework that encapsulates the multifaceted ways companies experiment within the metaverse. It extends the understanding of digital-physical convergence in service research, offering theoretical and practical insights into the development of phygital service ecosystems.

Este estudio tiene como objetivo investigar el concepto en evolución del Metaverso y sus implicaciones para la innovación en servicios. Busca comprender cómo la integración de tecnologías como la realidad extendida, la cadena de bloques, la inteligencia artificial y los tokens no fungibles permite a las empresas experimentar e innovar.

Adoptando una metodología cualitativa, esta investigación realiza una netnografía inmersiva a través de más de 25 estudios de casos que abarcan diversas industrias como los juegos, el comercio minorista, la atención médica y la educación. Se emplea el método de análisis temático para destilar conocimientos críticos, brindando un profundo análisis de los habilitadores tecnológicos, los procesos de innovación y las adaptaciones al mercado dentro del Metaverso.

El estudio aborda cuatro bloques principales a través de los cuales las empresas experimentan con el Metaverso para fomentar la innovación: habilitar la agencia de identidades virtuales, desarrollar tokens no fungibles, diseñar senderos inmersivos y crear micromundos físico-digitales. Estos dan forma al Metaverso mediante la actuación de actores, recursos, procesos y ecosistemas físico-digitales. Las empresas obtienen resultados de aprendizaje de dicha experimentación e identifican desafíos de aprendizaje.

Esta investigación contribuye al cuerpo de conocimiento incipiente sobre el Metaverso y la innovación en servicios al proporcionar un marco integral que encapsula las diversas formas en que las empresas experimentan dentro del Metaverso. Amplía la comprensión de la convergencia digital-física en la investigación de servicios, ofreciendo ideas teóricas y prácticas sobre el desarrollo de ecosistemas de servicios físico-digitales.

这项研究旨在探讨元宇宙概念的演变以及其对服务创新的影响。其目标在于深入理解通过整合增强现实、区块链、人工智能以及非同质化代币等技术, 如何使企业得以进行实验和创新。

本研究利用定性研究方法, 进行了一项沉浸式网络民族志调查, 涵盖了跨越游戏、零售、医疗保健和教育等多个行业的25多个案例。通过主题分析方法, 提炼出关键见解, 深入探讨了元宇宙内的技术驱动因素、创新过程和市场适应性。

这项研究以四个主要方面为切入点, 探讨了企业在元宇宙中促进创新的方法：启用虚拟身份的代理、开发非同质化代币、设计沉浸式路径以及打造物理-数字微观世界。通过这些措施, 企业塑造了元宇宙, 涉及行动者、资源、过程和物理-数字生态系统的执行。在此类实验中, 企业积累了丰富的学习经验, 并面临了

本研究旨在建立一个全面的框架, 以拓展关于元宇宙和服务创新的新知识, 揭示企业在元宇宙中进行实验的多方面方式。它不仅扩展了服务研究领域中数字与物理融合的理解, 还提供了有关发展物理-数字服务生态系统的理论和实践见解。

Allocation of complex crowdsourcing tasks, which typically include heterogeneous attributes such as value, difficulty, skill required, effort required and deadline, is still a challenging open problem. In recent years, agent-based crowdsourcing approaches focusing on recommendations or incentives have emerged to dynamically match workers with diverse characteristics to tasks to achieve high collective productivity. However, existing approaches are mostly designed based on expert knowledge grounded in well-established theoretical frameworks. They often fail to leverage on user-generated data to capture the complex interaction of crowdsourcing participants’ behaviours. This paper aims to address this challenge.

The paper proposes a policy network plus reputation network (PNRN) approach which combines supervised learning and reinforcement learning to imitate human task allocation strategies which beat artificial intelligence strategies in this large-scale empirical study. The proposed approach incorporates a policy network for the selection of task allocation strategies and a reputation network for calculating the trends of worker reputation fluctuations. Then, by iteratively applying the policy network and reputation network, a multi-round allocation strategy is proposed.

PNRN has been trained and evaluated using a large-scale real human task allocation strategy data set derived from the Agile Manager game with close to 500,000 decision records from 1,144 players in over 9,000 game sessions. Extensive experiments demonstrate the validity and efficiency of computational complex crowdsourcing task allocation strategy learned from human participants.

The paper can give a better task allocation strategy in the crowdsourcing systems.