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This study is part of a participatory design research project and aims to develop and study pedagogical frameworks and tools for integrating computational thinking (CT) concepts and data science practices into elementary school classrooms.

This paper describes a pedagogical approach that uses a data science framework the research team developed to assist teachers in providing data science instruction to elementary-aged students. Using phenomenological case study methodology, the authors use classroom observations, student focus groups, video recordings and artifacts to detail ways learners engage in data science practices and understand how they perceive their engagement during activities and learning.

Findings suggest student engagement in data science is enhanced when data problems are contextualized and connected to students’ lived experiences; data analysis and data-based decision-making is practiced in multiple ways; and students are given choices to communicate patterns, interpret graphs and tell data stories. The authors note challenges students experienced with data practices including conflict between inconsistencies in data patterns and lived experiences and focusing on data visualization appearances versus relationships between variables.

Data science instruction in elementary schools is an understudied, emerging and important area of data science education. Most elementary schools offer limited data science instruction; few elementary schools offer data science curriculum with embedded CT practices integrated across disciplines. This research assists elementary educators in fostering children's data science engagement and agency while developing their ability to reason, visualize and make decisions with data.

The goal of this study is to explore how content creators engage in critical data literacies on TikTok, a social media site that encourages the creation and dissemination of user-created, short-form videos. Critical data literacies encompass the ability to reason with, critique, control, and repurpose data for creative uses. Existing work on critical data literacies on social media has focused on understanding of personal data, critique of data use, and strategies to protect privacy. This work focuses on how TikTok content creators repurpose data to construct their own narratives.

Through hashtag search, the authors created a corpus of 410 TikTok videos focused on discussing environmental and climate action, and qualitatively coded the videos for data literacies practices and video features (audio, footage, background images) that may support these practices.

Content creators engaged in multiple practices to attach meanings to data and situate environmental and climate action discourse in lived experiences. While there were instances of no data practices, we found cases where creators compiled different data sources, situated data in personal and local contexts, and positioned their experiences as data points to supplement or counter other statistics. Creators further leveraged the platform’s technical features, particularly the ability to add original audio and background images, to add narratives to the collective discourse.

This study presents a unique focus on examining critical data literacies on social media. Findings highlight how content creators repurpose data and integrate personal experiences. They illustrate platform features to support data practices and inform the design of learning environments.

This paper seeks to explore how Indian buying firms practise an open book (OB) policy in supplier relationships in three industries, i.e. the vehicle industry, the construction industry and the information technology industry. It also aims to study determinants of open book practices.

Application of the OB policy is operationalised as the nature of open book practices, the extent and stage of data disclosed, the form of data disclosed, the direction of data disclosed, the attitude towards cost data disclosed, and the purpose and conditions of data disclosed. Determinants of the open book policy comprise characteristics of the exchange, the product, the buyer, the supplier and the supply market. Qualitative data were collected in the form of 22 interviews with representatives of three buying organisations and several of their suppliers in order to build three case studies.

The results suggest that the OB policy is used by buyers for diverse purposes ranging from strategic to operational, for example value engineering at the product development stage, to ensure supplier margins, for self improvements and cost reductions, and country entry decisions. Similarly, the data shared ranged from narrow to wide in scope and scale. Suppliers' attitudes ranged from fairly neutral to very negative towards open books. Power asymmetries, the number of alternative suppliers, product performance characteristics and value, incentives offered, and buyer efforts were found to influence OB practices.

Experiences gained from employing an OB policy in vehicle manufacturing suggests that firms in other industries can better leverage the use of open books for joint problem solving, equitable profit sharing and supplier selection.

The paper highlights that an OB policy can have broader applicability than recognised by many past studies and indicates that it can serve purposes of strategic decision‐making. It can also be an integrated part of a buyer's risk reduction strategy. Further, the study provides specific recommendations for Indian companies with regard to the application of an open book policy.

Information practices become highly complex in biodiversity citizen science projects due to the projects’ large scale, distributed setting and vast inclusion of participants. This study aims to contribute to knowledge concerning what variations of information practices can be found in biodiversity citizen science and what these practices may mean for the overall collaborative biodiversity data production in such projects.

Fifteen semi-structured interviews were carried out with participants engaged with the Swedish biodiversity citizen science information system Artportalen. The empirical data were analysed through a practice-theoretical lens investigating information practices in general and variations of practices in particular.

The analysis shows that the nexus of biodiversity citizen science information practices consists of observing, identifying, reporting, collecting, curating and validating species as well as decision-making. Information practices vary depending on participants’ technical know-how; knowledge production and learning; and preservation motivations. The study also found that reporting tools and field guides are significant for the formation of information practices. Competition was found to provide data quantity and knowledge growth but may inflict data bias. Finally, a discrepancy between practices of validating and decision-making have been noted, which could be mitigated by involving intermediary participants for mutual understandings of data.

The study places an empirically grounded information practice-theoretical perspective on citizen science participation, extending previous research seeking to model participant activities. Furthermore, the study nuances previous practice-oriented perspectives on citizen science by emphasising variations of practices.

This paper aims to examine factors influencing the practices of big data analytics applications by commercial banks operating in Jordan and their bank performance.

A conceptual framework was developed in this regard based on a comprehensive literature review and the Technology–Environment–Organization (TOE) model. A quantitative approach was used, and the data was collected from 235 commercial banks’ senior and middle managers (IT, financial and marketers) using both online and paper-based questionnaires.

The results showed that the extent of the practices of big data analytics applications by commercial banks operating in Jordan is considered to be moderate (i.e. 60%). The results indicated that 61% of the variation on the practices of big data analytics applications by commercial banks could be predicated by TOE model. The organizational factors were found the most important predictors. The results also provide empirical evidence that the extent of practices of big data analytics applications has a positive influence on the bank performance. In the final section, research implications and future directions are presented.

This paper contributes to theory by filling a gap in the literature regarding the extent of the practices of big data analytics applications by commercial banks operating in developing countries, such as Jordan. It empirically examines the impact of the practices of big data analytics applications on bank performance.

The purpose of this paper is to develop a collaborative agent-based web architecture and an agent-based green IS assessment tool to aid information technology (IT) practitioners in data centers assess their current green information systems (IS) practice toward attaining sustainability.

The methodology comprises that the collaborative agent-based web architecture, agents’ algorithm and the green IS assessment tool, which is validated by employing focus group questionnaire targeting IT practitioners in seven Malaysian-based enterprises that have an in-house data centers. With 105 valid samples at hand, descriptive analysis and exploratory factor analysis was utilized to determine the applicability of the implemented agent-based green IS assessment tool.

Findings reveal that the agent-based green IS assessment tool possesses the capability to evaluate benchmark and rate enterprise data centers current green IS practice. Additional findings indicate that the agent-based green IS assessment tool provide suggestions on how green IS practice can be improved in enterprise data centers.

This study only collected data from 105 IT practitioners in enterprise data centers based in Malaysia; as such results from this research cannot be generalized to other countries. Moreover, the developed collaborative agents for green IS practice assessment can only be fully deployed after domain experts has added green IS practice assessment questions and alternative answers.

This study presents an autonomous agent-based green IS assessment tool that supports the assessment of enterprise toward inclusion of sustainability considerations to enhance enterprise environmental performance.

This study provides empirical evidence for data centers efficacy leading toward a greener society for environmental conservation for future generations to come.

This study creates awareness by presenting the green IS practice to be implemented by IT practitioners in data centers. In addition, the agent-based green IS assessment tool provides a web-based platform for promoting environmental sustainability by supporting data centers toward evaluating, benchmarking and rating their current green IS practices.

This paper discusses how educational policies have shaped the development of large-scale educational data and reviews current practices on the educational data use in selected states. Our purposes are to: (1) analyze the common practice and use of educational data in postsecondary education institutions and identify challenges as the educational crossroads; (2) propose the concept of Data Literacy (DL) for teaching (Mandinach & Gummer, 2013a) and its relevance to researchers and stakeholders in postsecondary education; and (3) provide future implications for practices and research to increase educational DL among administrators, practitioners, and faculty in postsecondary education.

We used two guiding conceptual frameworks to analyze the common practice and use of educational data in postsecondary education institutions and identify challenges as the educational crossroads. First, we used the 4Vs of Big Data by Rajan (2012) to examine the misalignment between the policy mandate and the practices. The elements of the 4Vs of Big Data – volume, velocity, variety, and veracity – help us to depict how Big Data enables educators to organize, store, manage, and manipulate vast amounts of educational data at the right moment and at the right time. Second, we used the conceptual framework for DL proposed by Gummer and Mandinach (in press). They interpret DL “as the collection, examination, analysis, and interpretation of data to inform some sort of decision in an educational setting” (p. 1, in press).

Using the guiding frameworks, we identified four educational data crossroads as follows:

Crossroad 1: Unintended Increase in Workload Volume;

Crossroad 2: Unrealistic Expectations of Data Velocity;

Crossroad 3: Data Variety in Silos; and

Crossroad 4: Data Veracity and Policy Agenda Mismatch.

In this paper, we explain each of these crossroads in more detail with some examples.

Much of the existing body of literature, exemplary practices, as well as federal and state funding has been focused on K-12 education contexts. In this paper, we identify current practices and challenges of educational data in the institutions of higher education. Additionally, this paper presents the application of the exemplary practices of data literacy development in postsecondary education and implications for future practices of data literacy development in postsecondary education.

The purpose is to present proposals to foster what we call a socially meaningful transparency practice that aims to enhance public understanding of data-based systems through the production of accounts that are relevant and useful to diverse publics, and society more broadly.

The authors’ proposals emerge from reflections on challenges they experienced producing written and visual accounts of specific public sector data-based systems for research purposes. Following Ananny and Crawford's call to see limits to transparency practice as “openings”, the authors put their experience into dialogue with the literature to think about how we might chart a way through the challenges. Based on these reflections, the authors outline seven proposals for fostering socially meaningful transparency.

The authors identify three transparency challenges from their practice: information asymmetry, uncertainty and resourcing. The authors also present seven proposals related to reduction of information asymmetries between organisations and non-commercial external actors, enhanced legal rights to access information, shared decision making about what gets made transparent, making visible social impacts and uncertainties of data-systems, clear and accessible communication, timing of transparency practices and adequate resourcing.

Socially meaningful transparency aims to enhance public understanding of data-based systems. It is therefore a necessary condition not only for informed use of data-based products, but crucially for democratic engagement in the development of datafied societies.

The paper contributes to existing debates on meaningful transparency by arguing for a more social, rather than individual, approach to imagining how to make transparency practice more meaningful. The authors do this through their empirical reflection on our experience of doing transparency, conceptually through our notion of socially meaningful transparency, and practically through our seven proposals.

While big data creates business value, knowledge on how value is created remains limited and research is needed to discover big data’s value mechanism. The purpose of this paper is to explore value creation capabilities of big data through an alignment perspective.

The paper is based on a single case study of a service division of a large Danish wind turbine generator manufacturer based on 18 semi-structured interviews.

A strategic alignment framework comprising human, information technology, organization, performance, process and strategic practices are used as a basis to identify 15 types of alignment capabilities and their inter-dependent variables fostering the value creation of big data. The alignment framework is accompanied by seven propositions to obtain alignment of big data in service processes.

The study demonstrates empirical anchoring of how alignment capabilities affect a company’s ability to create value from big data as identified in a service supply chain.

Service supply chains and big data are complex matters. Therefore, understanding how alignment affects a company’s ability to create value of big data may help the company to overcome challenges of big data.

The study demonstrates how value from big data can be created following an alignment logic. By this, both critical and complementary alignment capabilities have been identified.

This study aims to evaluate the practices of carbon management accounting (CMA) made by companies committed to sustainability in Australia’s four highest carbon-emitting industries, including electricity, transport, stationary energy and agriculture. The evaluation covers three CMA phases (i.e. data collection, interpretation and reporting).

This is a cross-sectional study using descriptive research. Data was collected using a questionnaire primarily derived from Burritt et al.’s (2002, 2011) CMA framework and suggestions from other references. The questionnaire includes a set of closed- and open-ended questions. Data was collected from 39 senior managers in the selected industries with direct knowledge and experience in their companies’ CMA practices.

The respondents disclose numerous different motivations for their companies to practise CMA and various ways of practising their CMA. This reflects diverse industry practices due to the absence of a generally accepted standard and different stages of organisational learning. The findings also show that the respondents perceived CMA practices as essential to enhancing their companies’ sustainability performance and overall reputation. However, the majority of the respondents showed little appetite for carbon emission disclosure.

The findings thoroughly describe the current CMA practices by companies committed to sustainability in Australia’s high carbon-emitting industries. Overall, the results show that while the respondents perceived CMA practices as essential for their companies’ sustainability performance and energy-saving, the CMA applications were inconsistent, along with some concerning results, such as a lack of assurance and accountability in the data validation and audit. These indicate the importance of policymakers to consider establishing CMA guidelines or standards to improve its practice. For any company, these findings can be used as learning materials to start or enhance CMA practice at their companies. A broader professional CMA community can strengthen the collective efforts to make CMA more robust.

The findings portray the perceptions of practitioners from Australia’s four highest carbon-emitting industries, indicating motivations to use CMA to understand their companies’ carbon footprint and reduce their companies’ environmental impacts.

The findings contribute to the limited literature in this area and offer several valuable insights regarding the current practice of CMA in Australia, focussing on high carbon-emission industries. It also encourages more research in this area using data from other industries or countries to develop comparative results and strengthen the literature. Future research using actual carbon emission information or a longitudinal approach could also evaluate the changes and progresses in CMA practices.