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Digital humanities aim to use a digital-based revolutionary new way to carry out enhanced forms of humanities research more effectively and efficiently. This study develops a character social network relationship map tool (CSNRMT) that can semi-automatically assist digital humanists through human-computer interaction to more efficiently and accurately explore the character social network relationships from Chinese ancient texts for useful research findings.

With a counterbalanced design, semi-structured in-depth interview, and lag sequential analysis, a total of 21 research subjects participated in an experiment to examine the system effectiveness and technology acceptance of adopting the ancient book digital humanities research platform with and without the CSNRMT to interpret the characters and character social network relationships.

The experimental results reveal that the experimental group with the CSNRMT support appears higher system effectiveness on the interpretation of characters and character social network relationships than the control group without the CSNRMT, but does not achieve a statistically significant difference. Encouragingly, the experimental group with the CSNRMT support presents remarkably higher technology acceptance than the control group without the CSNRMT. Furthermore, use behaviors analyzed by lag sequential analysis reveal that the CSNRMT could assist digital humanists in the interpretation of character social network relationships. The results of the interview present positive opinions on the integration of system interface, smoothness of operation, and external search function.

Currently, the system effectiveness of exploring the character social network relationships from texts for useful research findings by using the CSNRMT developed in this study will be significantly affected by the accuracy of recognizing character names and character social network relationships from Chinese ancient texts. The developed CSNRMT will be more practical when the offered information about character names and character social network relationships is more accurate and broad.

This study develops an ancient book digital humanities research platform with an emerging CSNRMT that provides an easy-to-use real-time interaction interface to semi-automatically support digital humanists to perform digital humanities research with the need of exploring character social network relationships.

At present, a real-time social network analysis tool to provide a friendly interaction interface and effectively assist digital humanists in the digital humanities research with character social networks analysis is still lacked. This study thus presents the CSNRMT that can semi-automatically identify character names from Chinese ancient texts and provide an easy-to-use real-time interaction interface for supporting digital humanities research so that digital humanists could more efficiently and accurately establish character social network relationships from the analyzed texts to explore complicated character social networks relationship and find out useful research findings.

The purpose of this research is to explore and define the digital maturity of events using the Industry 4.0 model (I4.0) to create a definition for Events 4.0 (E4.0) and to place various relevant technologies on a scale of digital maturity.

In a mixed methods approach, we carried out a qualitative social media analysis and a quantitative survey of tourism and events academics. These surveys and the thorough literature review that preceded them allowed us to map the digital technologies used in events to levels of a digital maturity model.

We found that engagement with technology at events and delegate knowledge satisfactorily coexists for and across a number of different experiential levels. However, relative to I4.0, event research and the events industry appear to be digitally immature. At the top of the digital maturity scale, E4.0 might be defined as an event that is digitally managed; frequently upgrades its digital technology; fully integrates its communication systems; and optimizes digital operations and communication for event delivery, marketing, and customer experience. We expect E4.0 to drive further engagement with digital technologies and develop further research.

This study has responded to calls from the academic literature to provide a greater understanding of the digital maturity of events and how events engage with digital technology. Furthermore, the research is the first to introduce the concept of E4.0 into the academic literature. This work also provides insights for events practitioners which include the better understanding of the digital maturity of events and the widespread use of digital technology in event delivery.

Digital markets are increasingly constructed by an interplay between (non)human market actors, i.e. through algorithms, but, simultaneously, fragmented through platformization. This study aims to explore how interactional dynamics between (non)human market actors co-codify markets through expressive and networked content across social media platforms.

This study applies digital methods as cross-platform analysis to analyze two data sets retrieved from YouTube and Instagram using the keywords “sustainable fashion” and #sustainablefashion, respectively.

The study shows how interactional dynamics between (non)human market actors, co-codify markets across two social media platforms, i.e. YouTube and Instagram. The authors introduce the notion of sticky market webs of connection, illustrating how these dynamics foster cross-platform market codification through relations of exteriority.

Research implications highlight the necessity to account for all involved entities, including digital infrastructure in digital markets and the methodological potential of cross-platform analyses.

Practical implications highlight considerations managers should take into account when designing market communication for digital markets composed of (non)human market actors.

Social implications highlight the possible effects of (non)human market co-codification on markets and consumer culture, and corresponding countermeasures.

This study contributes to an increased understanding of digital market dynamics by illuminating interdependent market co-codification dynamics between (non)human market actors, and how these dynamics (de)territorialize digital market assemblages through relations of exteriority across platforms.

As a result of the advancement of the online environment, several methodological proposals emerged to establish procedures for digital qualitative research. While the various online ethnography methods overlap, they are not equivalent in terms of their theoretical bases, procedures and goals. The purpose of this article is to add clarity to their main differences, depicting specificities, potentialities and limitations of each method.

This conceptual article results from an integrative literature review that brought together studies that proposed, debated or used qualitative research methods in the digital environment. The research focused on the primary indexed journals publishing cultural studies in the past 20 years.

The literature review highlights four methods – virtual ethnography, digital ethnography, netnography and the post-application programming interface ethnography. The integrative literature review adds clarity depicting the main premises and procedures of each method. The present analysis positions the different methods considering two dimensions: the focus on the boundaries of the group/culture investigated, and the focus on the platform agency, affordances and specific dynamics.

The article proposes a comparative framework outlining points of convergence and divergence to create a reference for researchers on topics of significance while designing and conducting a research study in a digital environment. This conceptual organization highlights and supports qualitative researchers on their methodological challenges.

Despite the interest in non-profit and sustainable ways of connecting farms to society, less is known about how to conduct this through digital technology and communication. Thus, the purpose of this paper is to discuss how to connect farms to society through digital technology and communication.

In-depth interviews of 15 Australian farmers were conducted in order to understand their perceptions of how to engage in digital forms of social entrepreneurship and thematic analysis techniques were utilized to understand the content from the interview transcripts.

The findings suggest that digital social farm entrepreneurship can be categorized into social bricoleurs, social constructionists and social engineers.

This typology helps to understand the contextual role farm entrepreneurs play in rural economies and their place in global societies.

Many Australian farms are in remote locations far from urban centers, which makes digital forms of social entrepreneurship an important way that farmers can promote social entrepreneurial ventures.

This paper highlights how there has been a growing interest in developing social entrepreneurship in Australian farms due to their connection with rural communities and environments.

Applying social exchange theory as the theoretical basis, this paper aims to examine the impacts of two forms of digital social interaction on social well-being and helping behavior of customers: moderator–customer interaction quality and customer–customer social support. Furthermore, this paper investigates customer exchange ideology as a moderator of these impacts.

This paper adopted a purposive sampling method for survey materials sent to customers of firm-hosted virtual communities (FHVCs) using a consumer panel service company. The self-administered survey was developed from existing scales, and 265 usable responses were obtained.

Both forms of digital social interaction within FHVCs positively impact social well-being, which in turn positively influences helping behavior in the community. Social well-being is decomposed into social integration and social contribution, and each partially mediates the impact of customer–customer social support and moderator–customer interaction quality on helping behavior. This finding provides greater explanatory power for the role that digital social interactions have in predicting customer helping behavior in an FHVC. In addition, an exchange ideology positively moderates the impact of customer–customer social support on helping behavior via social integration.

This paper demonstrates that resource exchange dynamics occur digitally within FHVCs, which then affect social well-being and helping behaviors in customers. From a practical point of view, this study indicates the potential that digital interactions have in generating social and economic value through helping behaviors.

This paper aims to establish how strategic target-market selection decisions are shaped, challenged and driven in response to the rapidly evolving technological landscape. The authors critically evaluate the implications of these changes for the role of marketers and the organizational function of marketing.

The research uses qualitative methods. Key-informant interviews are conducted among senior organizational practitioners within client-side organizations, digital agencies and strategic marketing consultancies, seeking to contrast their views.

The findings reveal an erosion of responsibility for the integrated strategic role of marketing decision-making. In particular, the authors reveal that the evolving digital landscape has precipitated a sense of crisis for marketers and the role of marketing within the firm. This extends beyond simply remedying a skills-gap and is triggering a transformation that has repercussions for the future of marketing and its practice, thus diminishing functional accountability.

The findings have long-term implications for marketing as a strategic organizational function of the firm and for marketing as a practice.

The study considers an increasingly digitalized marketplace and the associated impact of big data for the function of marketing. It reveals the changing scope of strategic marketing practice and functional accountability.

This study investigates the determinants of effective human and artificial intelligence (AI) relationship-building strategies for brands. It explores the antecedents and consequences of consumers' interactant satisfaction with communication and identifies ways to enhance consumer purchase intention via AI chatbot promotion.

Microsoft Xiaoice served as the focal AI chatbot, and 331 valid samples were obtained. A two-stage structural equation modeling-artificial neural network approach was adopted to verify the proposed theoretical model.

Regarding the IQ (intelligence quotient) and EQ (emotional quotient) of AI chatbots, the multi-dimensional social support model helps explain consumers' interactant satisfaction with communication, which facilitates affective attachment and purchase intention. The results also show that chatbots should emphasize emotional and esteem social support more than informational support.

Brands should focus more on AI chatbots' emotional and empathetic responses than functional aspects when designing dialogue content for human–AI interactions. Well-designed AI chatbots can help marketers develop effective brand promotion strategies.

This research enriches the human–AI interaction literature by adopting a multi-dimensional social support theoretical lens that can enhance the interactant satisfaction with communication, affective attachment and purchase intention of AI chatbot users.

The purpose of this paper is to introduce Digital Methods Initiative Twitter Capture and Analysis Toolset, a toolset for capturing and analyzing Twitter data. Instead of just presenting a technical paper detailing the system, however, the authors argue that the type of data used for, as well as the methods encoded in, computational systems have epistemological repercussions for research. The authors thus aim at situating the development of the toolset in relation to methodological debates in the social sciences and humanities.

The authors review the possibilities and limitations of existing approaches to capture and analyze Twitter data in order to address the various ways in which computational systems frame research. The authors then introduce the open-source toolset and put forward an approach that embraces methodological diversity and epistemological plurality.

The authors find that design decisions and more general methodological reasoning can and should go hand in hand when building tools for computational social science or digital humanities.

Besides methodological transparency, the software provides robust and reproducible data capture and analysis, and interlinks with existing analytical software. Epistemic plurality is emphasized by taking into account how Twitter structures information, by allowing for a number of different sampling techniques, by enabling a variety of analytical approaches or paradigms, and by facilitating work at the micro, meso, and macro levels.

The paper opens up critical debate by connecting tool design to fundamental interrogations of methodology and its repercussions for the production of knowledge. The design of the software is inspired by exchanges and debates with scholars from a variety of disciplines and the attempt to propose a flexible and extensible tool that accommodates a wide array of methodological approaches is directly motivated by the desire to keep computational work open for various epistemic sensibilities.

This paper aims to complement the current understanding about user engagement in electronic word-of-mouth (eWoM) communications across online services and product communities. It examines the effect of the senders’ prior experience with products and services, and their extent of acquaintance with other community members, on user engagement with the eWoM.

The study used a sample of 576 unique user postings from the corporate fan page of two German firms: a service community of a telecom provider and a product community of a car manufacturer. Multiple regression analysis is used to test the conceptual model.

Senders’ prior experience and acquaintance positively affect user engagement with eWoM, and these effects differ across communities for products and services and across their influence on “likes” and “comments”. The results also suggest that communities for products are orientated toward information sharing, while those discussing services engage in information building.

This research explains mechanisms of user engagement with eWoM and opens directions for future research around motives, content and social media tools within the structures of online communities. The insights on information-handling dimensions of online tools and antecedents to their use contribute to the research on two prioritized topics by the Marketing Science Institute – “Measuring and Communicating the Value of Online Marketing Activities and Investments” and “Leveraging Digital/Social/Mobile Technology”.

This research offers insights for firms to leverage user engagement and facilitate eWoM generation through members who have a higher number of acquaintances or who have more experience with the product or service. Executives should concentrate their community engagement strategies on the identification and utilization of power users. The conceptualization and empirical test about the role of likes and comments will help social media managers to create and better capture value from their social media metrics.

The insights about the underlying factors that influence engagement with eWoM advance our understanding about the usage of online content.