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This study aims to identify and outline the critical challenges affecting the inclination of executives to use data as the basis for making decisions at a local government level.

The study conducted in-depth interviews with 21 public officials comprising middle- and top-level executives from 18 agencies and offices at the Bojonegoro Regency, one of Indonesia’s most progressive regencies in pursuing open government and smart cities.

The findings demonstrate that ensuring a good quality data architecture, nurturing data culture and developing analytics capability are essential in the case of a developing country such as Indonesia. However, insufficient policies and regulations, a nonexistent evaluative framework for data quality, disruptive local tradition and the ingrained autocratic administration represent significant and unique challenges to implementing data-driven decision-making in the local government in Indonesia.

The chosen research approach may result in a need for more generalizability beyond Indonesia, accentuating the necessity for the geographical objects to include other developing countries in future research.

The findings showcase that lack of awareness and acceptance from public officials and the general public of the importance of a data-driven approach; as such, a better understanding of the change in attitudes and mindsets of public officials is invariably one of the critical practical determinants.

The findings signify the importance of creating robust accountability systems and evaluative frameworks that consider the many variables influencing decisions that capture the significance of organizational and local culture.

The purpose of this study is to depict the value added by digital transformation to intellectual capital (IC) by virtue of the studies reached by the literature review on different databases are examined.

Journal of Intellectual Capital, which has the highest number of records from the resources included in the “Web of Science” content and covering the title of “intellectual capital” has been selected in this study. Research using bibliometric analysis has been conducted and it has been determined that the terms “digital transformation” and “intellectual capital” should be searched for simultaneously in each and every article published in the journal between the years 1975 and 2022.

A bibliometric analysis and citation mapping process are carried out considering all dimensions to reach the results and interpretation of findings. VOSviewer is used to visualize the bibliometric networks of results and findings in the form of scientific mapping, as well as to visualize the co-authorship analysis of keywords, co-authorship analysis and citation networks.

Bibliometric analysis is a method that can be used to evaluate the performance of a single journal. However, it is important to note that bibliometric analysis has some limitations when it comes to assessing the validity of a single journal. This circumstance is elaborately described as a limitation of this study. Bibliometric analysis is a method that can be used to evaluate the performance of a single journal. However, it is important to note that bibliometric analysis has some limitations when it comes to assessing the validity of a single journal. One limitation is that bibliometric analysis is based on quantitative metrics, such as citation counts, which do not take into account the quality of the research. Therefore, bibliometric analysis alone may not provide a complete picture of the validity of a single journal. In addition, bibliometric analysis is based on the number of times a paper is cited, which can be influenced by factors such as the prestige of the journal, the field of research and the time since the publication. In conclusion, bibliometric analysis can be used to evaluate the performance of a single journal, but it is important to consider its limitations.

This study identified contributions, gaps and limits based on the results of a bibliometric analysis. Italy is the most influential country and the issue is structured around four clusters: IC; digital transformation; human capital; and knowledge management. As previously unexplored issues are addressed in an innovative manner, it is acceptable to underline the paper’s originality.

This study aims to analyse and understand the current state of research in the field of digital marketing in “library and information science”.

This study used a “bibliometric research design.” A lexical title search was used to obtain the required data set for executing this study, and a comprehensive “indexing and abstracting” database, Web of Science, was used as a data harvesting source. Louvain’s clustering algorithm was used for network metrics.

The findings revealed that research productivity and impact have grown considerably over time, indicating significant attention towards digital marketing research in library and information science (LIS). Moreover, the results showed that the overall author collaboration patterns were weak, hence creating room for development in the author’s collaboration patterns.

The current study could be very beneficial in providing a comprehensive and up-to-date overview of the “digital marketing” research field scholarly output in LIS, which can be used by researchers, practitioners and policymakers to guide their work and make informed decisions.

The originality of this bibliometric study lies in its comprehensive and up-to-date analysis of the current state of research in the field of “digital marketing” in LIS. This study provides a unique and in-depth understanding of the key authors, venues and papers in the field, as well as the trends and patterns in the research.

This paper aims to address the limitations in designing educational approaches that apply critical approaches to data literacy, given the obscure nature of digital platforms, which leave youth unable to develop discourses that challenge dominant narratives about the role of data in their lives. The purpose of this study is to propose and evaluate a critical data literacy approach that empowers youth to engage with data from a sociocultural perspective using a speculative participatory research approach that affords opportunities to develop alternative discourses.

This is a multiple-case study that involves five alternative schools in Uruguay which implemented the Nayah-Irú curriculum over ten weeks leading to the development of six distinct research projects about the materialization of data in youth lives. The curriculum incorporates an alternate reality game (ARG) to engage youth in critical data literacy, based on the principles of Youth Participatory Action Research (YPAR) epistemology and Speculative Civic Literacies.

The findings of this study highlight the integration of speculative storytelling and real-life experiences in developing alternative discourses about datafication. The analysis revealed instances of discursive closure among the youth, but through the curriculum's speculative fiction elements, such as the narrative of Nayah-Irú, emotional connections were formed, leading to increased engagement, critical inquiry, and problem framing.

The study conducted on the Nayah-Irú curriculum shows its effectiveness in engaging youth and educators in critical data literacy by affording opportunities for youth to engage in the analysis of their personal data literacies in an alternative world. Bringing speculative approaches to data literacy can open new avenues for exploring data literacy with youth in ways that center their voices and help them overcome different forms of discursive closure.

This study offers new insights into critical data literacy education blending youth participatory action research epistemologies with a speculative literacies framework to support youth in developing alternative discourses regarding the role of data in their lives.

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

After the completion of this case study, students will be able to understand the importance of being close to local people when embarking on social business; understand that clear purpose and good decision-making can lead to great outcomes; and learn that innovation is crucial to ensure sustainability of both business and impact.

The case highlights the journey of Laiterie du Berger (LDB), a social enterprise in the agribusiness industry and the challenges faced as it expands and innovates. LDB’s roots lie in its commitment to social impact, aiming to uplift the Fulani livestock farmers and address socioeconomic issues. The company’s business model prioritizes people over profits, focusing on sustainable development and poverty alleviation. The LDB case showcases the challenges and opportunities in the agribusiness industry. LDB’s commitment to social impact, demonstrated through its support for farmers and sustainable farming practices, has been integral to its success. As the company expands and innovates, it faces critical decisions that require balancing financial growth with social responsibility. By embracing development, innovation and collaboration, LDB can continue to be a catalyst for positive change in the agribusiness industry while staying true to its roots and the principles that have defined its journey.

This case study is designed for bachelor’s and master’s degree students in the field of entrepreneurship and innovation, as well as MBA students. The case focuses on social entrepreneurship with the example of an agribusiness company located in Senegal, prioritizing social impact and quality of life. The case study explores the dynamics of the sector, including expansion strategy, innovation initiatives and the dilemma of balancing social mission and profit that social entrepreneurs may be facing. By analyzing this real-world situation of LDB, students will have the opportunity to enhance their decision-making skills.

Teaching notes are available for educators only.

CSS 3: Entrepreneurship

This research investigates the critical role of data governance (DG) in shaping a data-driven culture (DDC) within organizations, recognizing the transformative potential of data utilization for efficiency, opportunities, and productivity. The study delves into the influence of DG on DDC, emphasizing the mediating effect of data literacy (DL).

The study empirically assesses 125 experienced managers in Indonesian public service sector organizations using a quantitative approach. Structural Equation Modeling (SEM) analysis was chosen to examine the impact of DG on DDC and the mediating effects of DL on this relationship.

The findings highlight that both DG and DL serve as antecedents to DDC, with DL identified as a crucial mediator, explaining a significant portion of the effects between DG and DDC.

Beyond unveiling these relationships, the study discusses practical implications for organizational leaders and managers, emphasizing the need for effective policies and strategies in data-driven decision-making.

This research fills an important research gap by introducing an original model and providing empirical evidence on the dynamic interplay between DG, DL, and DDC, contributing to the evolving landscape of data-driven organizational cultures.

This paper aims to investigate the literacy corpus regarding the potential of big data to improve public decision-making processes and direct these processes toward the creation of public value. This paper presents a map of current knowledge in a sample of selected articles and explores the intersecting points between data from the private sector and the public dimension in relation to benefits for society.

A bibliometric analysis was performed to provide a retrospective review of published content in the past decade in the field of big data for the public interest. This paper describes citation patterns, key topics and publication trends.

The findings indicate a propensity in the current literature to deal with the issue of data value creation in the private dimension (data as input to improve business performance or customer relations). Research on data for the public good has so far been underestimated. Evidence shows that big data value creation is closely associated with a collective process in which multiple levels of interaction and data sharing develop between both private and public actors in data ecosystems that pose new challenges for accountability and legitimation processes.

The bibliometric method focuses on academic papers. This paper does not include conference proceedings, books or book chapters. Consequently, a part of the existing literature was excluded from the investigation and further empirical research is required to validate some of the proposed theoretical assumptions.

Although this paper presents the main contents of previous studies, it highlights the need to systematize data-driven private practices for public purposes. This paper offers insights to better understand these processes from a public management perspective.

Drawing on social capital theory, this study aims to explore the effect of corporate social responsibility (CSR) on organizational resilience. The research investigates the mediating role of relationship quality in the association of CSR with organizational resilience, and the moderating role of data-driven culture in the association between CSR and relationship quality.

Data were collected from Chinese agricultural firms with a sample of 241 senior or middle executives and structural equation modeling was used to test the research model and hypotheses.

The results indicate that CSR positively affects the relationship quality between agribusinesses and farmers, which in turn positively affects both proactive resilience and reactive resilience. Relationship quality has a partial mediating role in the association of CSR with proactive resilience and reactive resilience. Data-driven culture has a positive moderating effect on the relationship between CSR and relationship quality.

By arguing for CSR toward organizational resilience and analyzing its underlying mechanism, this study enriches the literature on CSR and organizational resilience and expands the existing knowledge on the roles of relationship quality and data-driven culture. This study also provides practical insights into how to improve organizational resilience.

Our study examines how artificial intelligence (AI) can enhance decision-making processes to promote circular economy practices within the utility sector.

A unique case study of Alia Servizi Ambientali Spa, an Italian multi-utility company using AI for waste management, is analyzed using the Gioia method and semi-structured interviews.

Our study discovers the proactive role of the user in waste management processes, the importance of economic incentives to increase the usefulness of the technology and the role of AI in waste management transformation processes (e.g. glass waste).

The present study enhances the circular economy model (transformation, distribution and recovery), uncovering AI’s role in waste management. Finally, we inspire managers with algorithms used for data-driven decisions.