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This study assesses whether the strategic orientation of financial institutions to provide value to customers influences the dimensions of personal data privacy perceived by consumers of banking services. We also analysed whether these dimensions directly influence the value in use and, indirectly, the reputation of financial institutions.

Based on the literature, a model was developed to verify the proposed relationships. To test the model, we collected data via an online questionnaire from 2,422 banking customers, with analysis using structural equation modelling with partial least squares estimation.

The results suggest that strategic value orientation tends to have a direct positive influence on the constructs knowledge, control, willingness to value privacy and trust in sharing personal information and a direct negative influence on the personal data privacy experience. Three dimensions of personal data privacy (knowledge, willingness to value privacy and trust in sharing personal information) tend to have a direct positive influence on value in use. The results showed that the dimensions of personal data privacy experience and control had a significant and negative impact on the value in use construct. Another finding is the positive influence of value in use on organizational reputation. Investing in strategic value orientation can generate consumer perceptions of personal data privacy, which is reflected in the value in use and reputation of banks.

This study is theoretically original because it brings up the organizational reputation of financial institutions based on the strategic orientation to offer value to customers, personal data privacy and the value in use of banking services. The study of these relationships is unprecedented in the literature.

This study aims to investigate the impact of ex-ante regulatory interventions on emerging digital markets related to data sharing and combination practices. Specifically, it evaluates how such interventions influence market contestability by considering data network effects and the economic value of data.

The research uses agent-based modeling and simulations to analyze the dynamics of value generation and market competition related to the regulatory obligations on data sharing and combination practices.

Results show that while the promotion of data sharing through data portability and interoperability has a positive impact on the market, restricting data combination may damage value generation or, at best, have no positive impact even when it is imposed only on those platforms with very large market shares. More generally, the results emphasize the role of regulators in enabling the market through interoperability and service multihoming. Data sharing through portability fosters competition, while the usage of complementary data enhances platform value without necessarily harming the market. Service provider multihoming complements these efforts.

Although agent-based modeling and simulations describe the dynamics of data markets and platform competition, they do not provide accurate forecasts of possible market outcomes.

This paper presents a novel approach to understanding the dynamics of data value generation and the effects of related regulatory interventions. In the absence of real-world data, agent-based modeling provides a means to understand the general dynamics of data markets under different regulatory decisions that have yet to be implemented. This analysis is timely given the emergence of regulatory concerns on how to stimulate a competitive digital market and a shift toward ex-ante regulation, such as the regulatory obligations to large gatekeepers set in the Digital Markets Act.

There is a widely held belief that open government data (OGD) have the potential to generate both economic and social value. This study aims to empirically unpack the relationship between OGD, diversification activities and innovation in the pursuit of economic value creation by firms.

Using a matched sample comparison method and difference-in-differences analyses, the authors study the impact of OGD on innovation over time in the USA. The authors considered the open government directive in the end of 2009 in the USA as a policy intervention and collected 10 years of financial data of 79 firms that use OGD and 79 matched control firms in the USA. The authors compare US firms using OGD, with matched control firms, regarding the firms’ level of product diversification as a measure of innovative use of OGD.

The authors provide empirical evidence that OGD policy contributes toward innovation, and hence economic value creation, through product diversification. Firms that leverage OGD show superior product diversification in comparison to the matching control firms. The results suggest that OGD contribute to firms’ innovation and pursuit of economic value, as evidenced by their increased product diversification.

Although the extant literature concerning OGD has underscored the impact of OGD on innovation and economic value generation, there is a lack of empirical evidence in the literature. This study seeks to add to the extant literature by providing empirical evidence that contributes to the understanding of the relationship between OGD, diversification and innovation in the pursuit of economic value creation.

Today’s one of the most important difficulties is tackling climate change and its effects on the environment. The Paris Agreement states that nations must balance the amount of greenhouse gases they emit and absorb until 2050 to contribute to the mitigation of greenhouse gases and to support sustainable development. According to the agreement, each country must determine, plan and regularly report on its contributions. Thus, it is important for the countries to predict and analyze their net zero performances in 2050. Therefore, the aim of this study is to evaluate European Continent Countries' net zero performances at the targeted year.

The European Continent Countries that ratified the Paris Agreement are specified as decision making units (DMUs). Input and output indicators are specified as primary energy consumption, freshwater withdrawals, gross domestic product (GDP), carbon-dioxide (CO₂) and nitrous-oxide (N₂O) emissions. Data from 1980 to 2019 are obtained and forecasted using autoregressive integrated moving average (ARIMA) until 2050. Then, the countries are clustered based on the forecasts of primary energy consumption and freshwater withdrawals using k-means algorithm. As desirable and undesirable outputs arise simultaneously, the performances are computed using Pure Environmental Index (PEI) and Mixed Environmental Index (MEI) data envelopment analysis (DEA) models.

It is expected that by 2050, CO₂ emissions of seven countries remain constant, N₂O emissions of seven countries remain stable and five countries’ both CO₂ and N₂O emissions remain constant. While it can be seen as success that many countries are expected to at least stabilize one emission, the likelihood of achieving net zero targets diminishes unless countries undertake significant reductions in emissions. According to the results, in Cluster 1, Turkey ranks last, while France, Germany, Italy and Spain are efficient countries. In Cluster 2, the United Kingdom ranks at last, while Greece, Luxembourg, Malta and Sweden are efficient countries.

In the literature, generally, CO₂ emission is considered as greenhouse gas. Moreover, none of the studies measured the net-zero performance of the countries in 2050 employing analytical techniques. This study objects to investigate how well European Continent Countries can comply with the necessities of the Agreement. Besides CO₂ emission, N₂O emission is also considered and the data of European Continent Countries in 2050 are estimated using ARIMA. Then, countries are clustered using k-means algorithm. DEA models are employed to measure the performances of the countries. Finally, forecasts and models validations are performed and comprehensive analysis of the results is conducted.

This study investigates the relationships between data governance (DG), business analytics capabilities (BAC), and decision-making performance (DMP), with a focus on the mediating effects of big data literacy (BDL) and data analytics competency (DAC).

The study was conducted with 178 experienced managers in public service organizations, using a quantitative approach. Structural equation modeling (SEM) and mediation tests were employed to analyze the data.

The findings reveal that DG and BDL are critical antecedents for developing analytical capabilities. Big data literacy mediates the relationship between DG and BAC, while BAC mediates the relationship between DG and DMP. Furthermore, DAC mediates the relationship between BA capabilities and DMP, explaining most of the effect of BAC on DMP.

These results highlight the importance of DG in fostering BDL and analytical skills for improved decision-making in organizations.

By prioritizing DG practices that promote BDL and analytical capabilities, organizations can leverage business analytics to enhance decision-making.

Personal data is today recognized as an asset in the digital economy, generating billion-dollar annual revenues for many companies. But how much value do users derive from their seemingly free apps (zero-price services), and what user costs are associated with this value exchange? By adopting a human-centric lens, this article scrutinizes the complex trade-offs users face trying to capture the benefits and unperceived costs that such usage entails.

Using a mixed-method research design, this study is anchored in empirical survey data from 196 participants in Linköping, Sweden. The authors investigate users’ willingness to pay for these services in relation to different types of costs.

The results indicate that users can derive significant value from the use of free services, which can be interpreted as a win-win situation between users and companies. Regarding costs, this research shows that the most significant costs for users are associated with procrastination, sleep deprivation and reduced focus, which can be challenging to identify and evaluate from the users’ perspective.

This study shows that zero-price services provide significant benefits like enhancing social connectivity and offering a wide variety of content. Significant drawbacks, such as increased procrastination and sleep disturbances, highlight the psychological effects of these platforms. These impacts include behavioral changes, emphasizing the influence of online platforms on user engagement. Furthermore, a trend toward single-purchase preferences over free services suggests changing consumer attitudes toward digital payment models. This underscores the need for further research on non-monetary aspects in zero-price markets for better understanding and regulation of the digital economy.

This study shows that users appreciate the accessibility and potential of zero-price services but are wary of privacy concerns. It underscores the need for companies to balance profit objectives with user experiences and privacy requirements. Offering a range of ad-free premium services to meet diverse customer needs can be effective. Users’ high valuation of privacy and transparency suggests businesses should focus on human-centric, privacy-respecting strategies. Increased transparency in data usage and giving users greater data control could enhance the user experience and foster sustainable customer relationships.

The study calls for policymakers to focus on non-monetary risks of zero-price services, such as behavioral changes and digital well-being impacts. They should consider implementing regulations to protect users, especially children, from manipulative designs such as “dark patterns”. Policymakers must balance user protection with innovation, leading to a sustainable zero-price economy. For zero-price service users, awareness of non-monetary costs, like procrastination and sleep deprivation, is vital. Understanding that “free” services have hidden costs is important, especially for younger generations. Managing privacy settings and selective service choices can protect privacy and well-being.

This research shifts the focus from simply valuing personal data based on market prices to assessing the worth of free services themselves. By listing various hidden costs, it underscores the need for increased user awareness and greater corporate transparency. Uniquely, it finds that users prefer making one-time purchases over using zero-price services, extending prior assumptions in the field. Additionally, it also characterizes the zero-price economy ecosystem, highlighting differences between market types and provides a deeper understanding of the zero-price market and its related concepts.

The data economy, emerging from the current hyper-technological landscape, is a global digital ecosystem where data is gathered, organized and exchanged to create economic value. This paper aims to shed light on the interplay of the different topics involved in the data economy, as found in the literature. The study research provides a comprehensive understanding of the opportunities, challenges and implications of the data economy for businesses, governments, individuals and society at large, while investigating its impact on business value creation, knowledge and digital business transformation.

The authors conducted a literature review that generated a conceptual map of the data economy by analyzing a corpus of research papers through a combination of machine learning algorithms, text mining techniques and a qualitative research approach.

The study findings revealed eight topics that collectively represent the essential features of data economy in the current literature, namely (1) Data Security, (2) Technology Enablers, (3) Business Implications, (4) Social Implications, (5) Political Framework, (6) Legal Enablers, (7) Privacy Concerns and (8) Data Marketplace. The study resulting model may help researchers and practitioners to develop the concept of data economy in a structured way and provide a subset of specific areas that require further research exploration.

Practically, this paper offers managers and marketers valuable insights to comprehend how to manage the opportunities deriving from a constantly changing competitive arena whose value is today also generated by the data economy.

Socially, the authors also reveal insights explaining how the data economy features may be exploited to build a better society.

This is the first paper exploring the data economy opportunity for business value creation from a critical perspective.

This paper aims to propose a solution for dissolving the “privacy paradox” in social networks, and explore the feasibility of adopting a synergistic mechanism of “deep-learning algorithms” and “differential privacy algorithms” to dissolve this issue.

To validate our viewpoint, this study constructs a game model with two algorithms as the core strategies.

The “deep-learning algorithms” offer a “profit guarantee” to both network users and operators. On the other hand, the “differential privacy algorithms” provide a “security guarantee” to both network users and operators. By combining these two approaches, the synergistic mechanism achieves a balance between “privacy security” and “data value”.

The findings of this paper suggest that algorithm practitioners should accelerate the innovation of algorithmic mechanisms, network operators should take responsibility for users’ privacy protection, and users should develop a correct understanding of privacy. This will provide a feasible approach to achieve the balance between “privacy security” and “data value”.

These findings offer some insights into users’ privacy protection and personal data sharing.

The purpose of this research is to provide a framework in which new data quality dimensions are defined. The new dimensions provide new metrics for the assessment of lecture video indexing. As lecture video indexing involves various steps, the proposed framework containing new dimensions, introduces new integrated approach for evaluating an indexing method or algorithm from the beginning to the end.

The emphasis in this study is on the fifth step of design science research methodology (DSRM), known as evaluation. That is, the methods that are developed in the field of lecture video indexing as an artifact, should be evaluated from different aspects. In this research, nine dimensions of data quality including accuracy, value-added, relevancy, completeness, appropriate amount of data, concise, consistency, interpretability and accessibility have been redefined based on previous studies and nominal group technique (NGT).

The proposed dimensions are implemented as new metrics to evaluate a newly developed lecture video indexing algorithm, LVTIA and numerical values have been obtained based on the proposed definitions for each dimension. In addition, the new dimensions are compared with each other in terms of various aspects. The comparison shows that each dimension that is used for assessing lecture video indexing, is able to reflect a different weakness or strength of an indexing method or algorithm.

Despite development of different methods for indexing lecture videos, the issue of data quality and its various dimensions have not been studied. Since data with low quality can affect the process of scientific lecture video indexing, the issue of data quality in this process requires special attention.

With the arrival of the big data era, governments have appointed a chief data officer (CDO) to meet the opportunities and challenges brought by big data. The existing research on the CDOs is very limited, and what does exist focuses primarily on what are CDOs do. Little research has explored how CDOs do. To fill this gap, this study employed ambidexterity theory to investigate the ambidexterity of CDOs’ impact on data-driven innovation.

To empirically test the model, a survey study was conducted to empirically test the model. Data were collected from 261 CDOs in government and government employees in big data management centers or bureaus. The collected data were analyzed quantitatively to answer hypotheses using a structural equation model.

The findings suggest that data exploitation and data exploration significantly influence data-driven leadership, culture and value propositions. Data-driven leadership and value propositions significantly impact government performance.

This study is one of the first attempts to investigate how CDOs work, especially when promoting data-driven innovation. In addition, this study extends ambidexterity theory into the issue of the CDO in government.