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While social media data are used increasingly in studies of social movements, social media evolves far more rapidly than academic research and publication. This chapter argues that researchers should adopt historical and archival approaches to social media data. Treating social media data as an “instant archive” – one that is self-curated, is co-constituted, and changes rapidly – we caution researchers to pay attention to the features of this archive and their implications for working with the data therein. Applying insights from recent discussions of archival methods for social science research to the specific features of social media data, we explore how platform features, repressive effects, and user innovations affect the content of the instant archive. We then offer strategies for researchers' methodological approaches, including how best to select units of analysis and platforms, how to collect and interpret archival materials, and how to identify silences in the data.

Drawing upon a contractarian lens of corporate social responsibility (CSR), this study aims to explore community construals of happiness and evaluates conceptual boundaries of CSR for happiness.

Using a mixed-methods design, natural language processing and thematic analysis techniques were used to analyse large volumes of textual survey data collected from over 1,000 research participants through an online survey.

Results indicated that lay construals of happiness were primarily defined in terms of socioeconomic conditions and psychoemotional experiences. In explicating the boundary conditions, community perceptions regarding the extent of businesses’ social responsibilities for happiness were evidenced in five themes: that businesses have a responsibility not to harm happiness, a responsibility to enable conditions for happiness to occur, a responsibility to exercise awareness of happiness implications in decision-making, a responsibility for happiness that is limited by strategic purpose and resource capability and a responsibility for happiness that is limited by stakeholder proximity.

This study contributes to the theoretical and empirical foundation of CSR for happiness while simultaneously developing and applying a novel approach for processing and analysing large volumes of qualitative survey-based data.

This paper aims to use text mining methods to explore the similarities and differences between countries’ national digital transformation (DT) and Industry 4.0 (I4.0) policies. The study examines the applicability of text mining as an alternative for comprehensive clustering of national I4.0 and DT strategies, encouraging policy researchers toward data science that can offer rapid policy analysis and benchmarking.

With an exploratory research approach, topic modeling, principal component analysis and unsupervised machine learning algorithms (k-means and hierarchical clustering) are used for clustering national I4.0 and DT strategies. This paper uses a corpus of policy documents and related scientific publications from several countries and integrate their science and technology performance. The paper also presents the positioning of Türkiye’s I4.0 and DT national policy as a case from a developing country context.

Text mining provides meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, aligned with their geographic, economic and political circumstances. Findings also shed light on the DT strategic landscape and the key themes spanning various policy dimensions. Drawing from the Turkish case, political options are discussed in the context of developing (follower) countries’ I4.0 and DT.

The paper reveals meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, reflecting political proximities aligned with their geographic, economic and political circumstances. This can help policymakers to comparatively understand national DT and I4.0 policies and use this knowledge to reflect collaborative and competitive measures to their policies.

This paper provides a unique combined methodology for text mining-based policy analysis in the DT context, which has not been adopted. In an era where computational social science and machine learning have gained importance and adaptability to political and social science fields, and in the technology and innovation management discipline, clustering applications showed similar and different policy patterns in a timely and unbiased manner.

This study aims to explore the implementation of artificial intelligence (AI) in archival practice by presenting the thoughts and opinions of working archival practitioners. It contributes to the extant literature with a fresh perspective, expanding the discussion on AI adoption by investigating how it influences the perceptions of digital archival expertise.

In this study a two-phase data collection consisting of four online focus groups was held to gather the opinions of international archives and digital preservation professionals (n = 16), that participated on a volunteer basis. The qualitative analysis of the transcripts was performed using template analysis, a style of thematic analysis.

Four main themes were identified: fitting AI into day to day practice; the responsible use of (AI) technology; managing expectations (about AI adoption) and bias associated with the use of AI. The analysis suggests that AI adoption combined with hindsight about digitisation as a disruptive technology might provide archival practitioners with a framework for re-defining, advocating and outlining digital archival expertise.

The volunteer basis of this study meant that the sample was not representative or generalisable.

Although the results of this research are not generalisable, they shed light on the challenges prospected by the implementation of AI in the archives and for the digital curation professionals dealing with this change. The evolution of the characterisation of digital archival expertise is a topic reserved for future research.

The digital environment afforded by social networks has created an opportunity to understand more clearly the impact of social media native advertising on advertising processing outcomes. Thus, the current study integrates native advertising with engagement literature to compare engagement outcomes between feed and banner placements before analyzing engagement outcomes of sponsored social media posts by advertising objective. This work aims to contribute to advertising effectiveness literature arguing for the importance of engagement as a measure of effectiveness.

Facebook advertising data were collected from a convenience sample of 10 Facebook advertisers that accounted for roughly $414,000 in advertising spend. Panel data, which are also called longitudinal or cross-sectional time-series data, used 26 months of data from the 10 advertisers to measure relationships between native advertising exposure and digital consumer engagement with advertising by advertising objectives of brand awareness, link clicks, conversions, post-engagement and video views.

Exposure to native advertising was a strong predictor of advertising processing and consumption using the three variables of interest: clicks, comments and shares. Ads reaching consumers while natively consuming content in their feed resulted in statistically significant improvements in impressions and clicks when compared to banner ads. Exposure to native ads was significantly related to all engagement outcomes of interest, except for advertisers who chose post-engagement as their advertising objective.

The results suggest that for advertisers seeking clicks, post-engagement objectives should likely be avoided. For this group, impressions were not related to link clicks but were related to comments and shares. Native advertising placements in the feed, however, are generally more effective than banner ads on Facebook for advertisers seeking engagement.

This research is one of few studies to use longitudinal advertising data to explore engagement effects using real-world data collected from a diverse set of Facebook advertisers over a 26-month period. This study shows that interactive marketers using a social media feed to reach consumers can expect positive outcomes in advertising consumption, affective and cognitive processing and advocacy, but those outcomes may vary by advertising objective.

Given the uniqueness of the data set, the findings contribute to native advertising literature and to the literature on digital consumer engagement with advertising in social media. The study also provides empirical support for the efficacy of native advertising.

As public health professionals strive to promote vaccines for inoculation efforts, fervent anti-vaccination movements are marshaling against it. This study is motived by a need to better understand the online discussion around vaccination. The authors identified the sentiments, emotions and topics of pro- and anti-vaxxers’ tweets, investigated their change since the pandemic started and further examined the associations between these content features and audiences’ engagement.

Utilizing a snowball sampling method, data were collected from the Twitter accounts of 100 pro-vaxxers (266,680 tweets) and 100 anti-vaxxers (248,425 tweets). The authors are adopting a zero-shot machine learning algorithm with a pre-trained transformer-based model for sentiment analysis and structural topic modeling to extract the topics. And the authors use the hurdle negative binomial model to test the relationships among sentiment/emotion, topics and engagement.

In general, pro-vaxxers used more positive tones and more emotions of joy in their tweets, while anti-vaxxers utilized more negative terms. The cues of sadness predominantly encourage retweets across the pro- and anti-vaccine corpus, while tweets amplifying the emotion of surprise are more attention-grabbing and getting more likes. Topic modeling of tweets yields the top 15 topics for pro- and anti-vaxxers separately. Among the pro-vaxxers’ tweets, the topics of “Child protection” and “COVID-19 situation” are positively predicting audiences’ engagement. For anti-vaxxers, the topics of “Supporting Trump,” “Injured children,” “COVID-19 situation,” “Media propaganda” and “Community building” are more appealing to audiences.

This study utilizes social media data and a state-of-art machine learning algorithm to generate insights into the development of emotionally appealing content and effective vaccine promotion strategies while combating coronavirus disease 2019 and moving toward a global recovery.

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-03-2022-0186

Artificial intelligence (AI) allows the brand to co-create value with young customers through mobile apps. However, as many brands claim that their mobile apps are using the most updated AI technology, young customers face app fatigue and start questioning the authenticity of this touchpoint. This paper aims to study the mediating effect of authenticity for the value co-creation of AI-powered branded applications.

Drawing from regulatory engagement theory, this study conceptualize authenticity as the key construct in customers’ value experience process, which triggers customer value co-creation. Two scenario-based online experiments are conducted to collect data from 444 young customers. Data analysis is performed using ANOVA and Process Hayes.

The results reveal that perceived authenticity is an important mediator between media richness (chatbot vs AI text vs augmented reality) and value co-creation. There is no interaction effect of co-brand fit (high vs low) and source endorsement (doctor vs government) on the relationship between media richness and perceived authenticity, whereas injunctive norms (high vs low) strengthen this relationship.

The finding provides insights for marketing managers on engaging young customers suffering from app fatigue. Authenticity holds the key to young customers’ technological perceptions.

This research highlights the importance of perceived authenticity in encouraging young customers to co-create value. Young customers consider authenticity as a motivational force experience that involves customers through the app’s attributes (e.g. media richness) and social standards (e.g. norms), rather than brand factors (e.g. co-brand fit, source endorsement).

The role of customers has moved from reactive to proactive, and they require more control over digital touchpoints. The quest for authenticity is their response to the dark side of interactive marketing – forms of faking, manipulation and exploitation. Authenticity becomes a key topic in interactive marketing as it reflects how customers assess digital touchpoints. However, there is a lack of comprehensive knowledge of authenticity in the interactive environment.

This article consolidates the authenticity studies in various digital touchpoints using the entity-referent correspondence framework. This research employs bibliometric analysis and thematic analysis of 103 articles in the last 15 years.

Five research clusters are identified: (1) human, (2) brand-generated content, (3) user-generated content, (4) branded platforms and (5) new technologies-based touchpoints (artificial intelligence, augmented reality and virtual reality). Most interactive marketing studies focus on human and content authenticity, and new technologies-based touchpoints lack comprehensive conceptualization. The review synthesizes the types of authenticity used in each touchpoint and highlights the importance of true-to-creator-self and true-to-customer-self in customer evaluation. We further propose a research framework with four antecedent groups and outcomes.

Our research supports managers by highlighting the type of authenticity prioritized in each touchpoint's development.

To answer the call from interactive marketing researchers, this research highlights the distinct definitions of authenticity at various digital touchpoints rather than looking at the overall brand. Trends, gaps and future research agenda of the authenticity concept in technology adoption and customer behavior are discussed.