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Allocating budget optimally to marketing channels is an increasingly difficult venture. This difficulty is compounded by an increase in the number of marketing channels, a rise in siloed data between marketing technologies, and a decrease in individually identifiable data due to legislated privacy policies. The authors explore the rich attribution modeling literature and discuss the different model types and approaches previously used by practitioners and researchers. They also investigate the changing landscape of marketing attribution, discuss the advantages and disadvantages of different data handling approaches (i.e., aggregate vs. individualistic data), and present a research agenda for future attribution research.

To cope with online competitors and new consumer behaviors, retailers need to hybrid digital and physical offerings to implement an omni-channel business model. This constitutes a digital transformation of the traditional business model. However, business cases on how traditional retailers are shifting from multi-channel to omni-channel retailing are lacking. This paper aims to explore the different issues and organizational paths during the transformation of a business model.

This study is based on a qualitative multiple case study of five retailers with a global reach currently implementing an omni-channel business model.

This research sheds light on three main issues encountered by retailers and the different underlying decisions when moving toward an omni-channel business model. The first relates to revenue attribution across channels, which involves rethinking traditional key performance indicators to give incentives to stores when promoting digital offers. The second issue concerns the supply chain decisions associated with cross-channel operations. The third issue relates to the delicate balance between global reach (digital channel) and local reach (specific store) for communication on social media and marketing decisions on pricing. This study provides empirical evidence about the variety of choices that retailers make to cope with the issues during the implementation of an omni-channel business model.

This work explores the issues faced by established firms when moving toward a new business model that is the hybridization of two existing business model managed separately. It provides comprehensive and clear illustration of how to manage such a business model transformation process that can be used by both business strategy practice and academic research.

The purpose of this study is to investigate coproduction behavior in networking alumni communities via the progress from platform belongingness, knowledge sharing and citizenship behavior. Alumni networking communities have emerged as valuable assets for conserving institutional resources, supporting members and contributing new resources for alumni-institutional professional development. However, the previous literature has not yet captured the explicit processes by which these contributions are made.

Data from 711 respondents selected from an alumni collaboration network were subjected to structural equation modeling analysis.

The study explored resource conservation (belongingness) as the primary relational mechanism for alumni to share their instrumental resources (knowledge sharing), supporting resources (citizenship behavior) and competent resources (coproduction behavior). Knowledge sharing and citizenship behavior act as intermediate agents to trigger coproduction behavior. The authors show how subjective norm, group norm and trust is regarded as a tool to reduce bonding intrusiveness (i.e. the intrusive side-effects of a bond) and moderate the indirect effect of belongingness on coproduction and the direct effect of citizenship on coproduction.

By applying attachment theory, conservation of resources theory and digital platform networking perspectives, this study describes major implications for designing inspiring and compatible community platforms.

Guidance is provided for improving sustainable alumni communities through citizenship-sharing and coproduction behavior.

Online alumni communities are regarded as resource conservators, which can result in valuable coproduction, via the sharing of knowledge, expertise and skillsets to create profit for a range of institutions and industries.

Alumni networking platforms encourage alumni cohesiveness, stimulate knowledge exchange and improve professionalism.

This study aims to explore knowledge structure and research trends in the domain of digital humanities (DH) in the recent decade. The study identified prevailing topics and then, analyzed trends of such topics over time in the DH field.

Research bibliographic data in the area of DH were collected from scholarly databases. Multiple text mining techniques were used to identify prevailing research topics and trends, such as keyword co-occurrences, bigram analysis, structural topic models and bi-term topic models.

Term-level analysis revealed that cultural heritage, geographic information, semantic web, linked data and digital media were among the most popular topics in the recent decade. Structural topic models identified that linked open data, text mining, semantic web and ontology, text digitization and social network analysis received increased attention in the DH field.

This study applied existent text mining techniques to understand the research domain in DH. The study collected a large set of bibliographic text, representing the area of DH from multiple academic databases and explored research trends based on structural topic models.

Considering rapid digitalization development, this study examines the impacts of digital technology innovation on social responsibility in platform enterprises.

The study applies the event study method and cross-sectional regression analysis, taking 168 digital technology innovations for social responsibility issued by 88 listed platform enterprises from 2011 to 2022 to study the impact of digital technology innovations for social responsibility announcements of different announcement content and platform attributes on the stock market value of platform enterprises.

The results show that, first, the positive stock market reaction is produced on the same day as the digital technology innovation announcement. Second, the announcement of the platform’s public social responsibility and the announcement of co-innovation and radical innovation bring more positive stock market reactions. In addition, the announcements mentioned above issued by trading platforms bring more positive stock market reactions. Finally, the social responsibility attribution characteristics of the announcement did not have a significant differentiated impact on the stock market reaction.

Most scholars have studied digital technology innovation for social responsibility through modeling rather than second-hand data to empirically examine. This study uses second-hand data with the instrumental stakeholder theory to provide a new research perspective on platform social responsibility. In addition, in order to explore the different impacts of digital technology innovation on social responsibility, this study has classified digital technology innovation for social responsibility according to its social responsibility and digital technology innovation characteristics.

The present study investigates whether individuals having a fixed digital mindset (comprises fundamental beliefs about technological ability and organizational resources as work becomes more digitalized) experience greater helplessness working in virtual teamwork environments. The authors examine how perceived internal human resource management (HRM) alignment moderates the positive relationship expected between individuals' fixed digital mindset and feelings of helplessness. Together, the paper aims to contribute to a greater understanding of the personal and contextual factors that influence an individual's experience of helplessness in virtual team settings.

The authors test the hypotheses using time-lagged survey data collected from 153 information technology (IT) engineers working in virtual teams in Europe.

The authors find that individuals with higher levels of fixed digital mindset experience greater helplessness in virtual teamwork environments than individuals with lower levels. Furthermore, the authors find that having higher-fixed beliefs about organizational resources is positively related to helplessness when individuals perceive that the broader HRM system is misaligned with the virtual teamwork environment.

The data were obtained from IT engineers in Europe, which is potentially limiting the generalizability of the authors' findings to other work contexts and cultures.

The authors' study helps leaders in virtual teamwork environments to better understand and manage the personal and contextual factors that could affect individuals' well-being and effective functioning in such settings.

The authors' research contributes to the scant literature investigating the personal characteristics important in virtual teamwork environments and the contextual factors important for aligning virtual teamwork designs with the organizational system. The authors extend this research by looking at personal and contextual factors together in a single model.

Allocation of the marketing budget has become increasingly challenging due to the diverse channel exposure to customers. This study aims to enhance global marketing knowledge by introducing an ensemble attribution model to optimize marketing budget allocation for online marketing channels. As empirical research, this study demonstrates the supremacy of the ensemble model over standalone models.

The transactional data set for car insurance from an Indian insurance aggregator is used in this empirical study. The data set contains information from more than three million platform visitors. A robust ensemble model is created by combining results from two probabilistic models, namely, the Markov chain model and the Shapley value. These results are compared and validated with heuristic models. Also, the performances of online marketing channels and attribution models are evaluated based on the devices used (i.e. desktop vs mobile).

Channel importance charts for desktop and mobile devices are analyzed to understand the top contributing online marketing channels. Customer relationship management-emailers and Google cost per click a paid advertising is identified as the top two marketing channels for desktop and mobile channels. The research reveals that ensemble model accuracy is better than the standalone model, that is, the Markov chain model and the Shapley value.

To the best of the authors’ knowledge, the current research is the first of its kind to introduce ensemble modeling for solving attribution problems in online marketing. A comparison with heuristic models using different devices (desktop and mobile) offers insights into the results with heuristic models.

This study investigated four different economic level areas of China (Shanghai, Shandong, Shaanxi, and Guizhou) to analyze the eastern and western urban and rural media service status at different development stages. This set of data comes from the comparison of regional urban and rural areas and indicates the various aspects of differences in the survey area, including the media use habits, media resources, media consumer demand, evaluation of media services, the role of media in public life, public knowledge level, and so on. On analyzing data comprehensively, one thing can be found that there is a positive correlation between the public media contact degree and the public knowledge level. The media plays an extremely important role in public life and regional public knowledge gap between urban and rural areas exists. Furthermore, this gap is positively correlated to the media resources and media exposure. The trend of media using on mobile phone and computer in urban areas increases significantly greater than in rural areas. Then, how to narrow the urban–rural and regional public knowledge gap and reduce the negative impact of the digital divide will be an important urgent task.

The purpose of this paper is to describe the creation of a catalogue of museum objects associated with two media art collections. The proposal was formulated under the Voremetur Project “Vocabularios para una Red de Archivos y Colecciones de Media Art y sus efectos: metaliteracy y turismo de conocimiento” (thesauri for networked media art archives and collections and their effects: metaliteracy and knowledge tourism) (HAR2016-75949-C2-1-R). Collection characteristics and typologies are discussed along with the difficulties encountered and the interoperability of the platform chosen with other Web resources that foster visibility.

This paper describes a case study and a review of the protocols and standards used to catalogue media art collections. Digitised descriptions were processed with Omeka software in conjunction with the expanded Dublin Core metadata schema. This paper also reviews the literature on the conceptualisation of these collections and the challenges involved in their conservation and management.

The result was the creation of a digital repository for two media art collections: one linked to Espacio P; and the other the outcome of digitising part of the MIDECIANT collection (Archivo Media ART AEMA).

The methodology innovates the description and analysis of museum objects on media art in Spain. The proposed cataloguing method can be replicated and used to describe similar collections and lays the grounds for creating a Spanish network of media art archives and collections.