Introducing a Data Perspective to Sustainability: How Companies Develop Data Sourcing Practices for Sustainability Initiatives

Many companies use the UN Sustainable Development Goals as a point of reference for their sustainability initiatives and actions. Reporting on these goals requires collecting, processing, and interpreting substantial amounts of data (e.g., on emissions or recycled materials) that were previously neither captured nor analyzed. Although prior studies have occasionally highlighted the issues of data availability, data access, and data quality, a research void prevails on the data perspective in the sustainability context. This article aims at developing this perspective by shedding light on data sourcing practices for the reliable reporting of sustainability initiatives and goals. We make a two-fold contribution to sustainability and Green IS research: First, as a theoretical contribution, we propose a framework based on institutional theory to explain how companies develop their data sourcing practices in response to regulatory, normative, and cultural-cognitive pressures. Second, our empirical contributions include insights into five case studies that represent key initiatives in the field of environmental sustainability that touch on, first, understanding the ecological footprint, and, second, obtaining labels or complying with regulations, both on product and packaging levels. Based on five case studies, we identify three data sourcing practices: sense-making, data collection, and data reconciliation. Thereby, our research lays the foundation for an academic conceptualization of data sourcing in the context of sustainability

A Method to Screen, Assess, and Prepare Open Data for Use

Open data's value-creating capabilities and innovation potential are widely recognized, resulting in a notable increase in the number of published open data sources. A crucial challenge for companies intending to leverage open data is to identify suitable open datasets that support specific business scenarios and prepare these datasets for use. Researchers have developed several open data assessment techniques, but those are restricted in scope, do not consider the use context, and are not embedded in the complete set of activities required for open data consumption in enterprises. Therefore, our research aims to develop prescriptive knowledge in the form of a meaningful method to screen, assess, and prepare open data for use in an enterprise setting. Our findings complement existing open data assessment techniques by providing methodological guidance to prepare open data of uncertain quality for use in a value-adding and demand-oriented manner, enabled by knowledge graphs and linked data concepts. From an academic perspective, our research conceptualizes open data preparation as a purposeful and value-creating process

Unleashing the Potential of External Data: A DSR-based Approach to Data Sourcing

External data has become an indispensable pillar in state-of-the-art decision-making and value creation in an enterprise context. Despite the increasing motivation to use external data, information systems (IS) research still lacks an adequate data sourcing perspective. This study aims to address this gap by investigating the practical challenges in this emerging field and developing a reference process for sourcing and managing external data. To this end, we adopt a design science research approach leveraging collaboration with practitioners from nine high-profile companies. Our findings contribute to the scarce body of knowledge on data sourcing in IS by proposing explicit prescriptions in the form of a reference process for sourcing and managing external data

Toward Cross-company Value Generation from Data: Investigating the Role of Data Sharing Communities

Without a doubt, data is considered as a strategic asset for the digital economy. While companies seek for greater data-driven insights to unlock new business opportunities, we observe a shift from internal to cross-company data sharing (Wixom, Sebastian and Gregory, 2020). The European data strategy (2020), and the underlying EU’s data act (2022) bring upfront numerous benefits of data sharing, such as improved access to private and public data, generation of new products and services, or reduction of public services’ costs, amounting to 270 billion euros in additional GDP by 2028. In fact, estimates from Gartner (2021) show that private organizations engaging in sharing their data can expect to generate three times more measurable economic benefit compared to those who do not. Data sharing also contributes to the sustainable use and reuse of data particularly in the context of reduction of energy use and technological resources (European Commission, 2020; Akhgarnush, 2021

Essays on External Data Sourcing

In the age of digital transformation, enterprises are becoming increasingly aware of the value of external data, which originates beyond their four walls. Despite the growing number of datasets and their potential value, external data is sourced in an ad-hoc manner without clear guidelines. This leads to inconsistent sourcing decisions, characterized by a lack of clarity on the object of sourcing and the underlying data sourcing practices. Existing studies showcase scenarios of enterprises using external data, which are fraught with obstacles. A crucial challenge confronting companies that intend to use external data is to identify suitable datasets supporting specific business scenarios and to prepare them for use. In the context of a specific external data type – open data in our case – researchers have developed several data assessment techniques. Unfortunately, these techniques are limited in scope, do not consider the use context, and are not embedded in the complete set of activities required for open data consumption in enterprises. The emerging field of data sourcing also displays a notable absence of comprehensive research, prompting a clarion call for action in Information Systems (IS) research to address this gap. Considering the abovementioned research opportunities, this thesis – through three interrelated research streams – provides foundations for, analyzes, and improves data sourcing practices in the enterprise context. The first stream lays the foundations for the topic and investigates the company-wide sourcing and managing of external data. The second stream reflects on sourcing practices concerning open data, as one of the most prominent external data types, and challenges the widespread perception that open data is easily accessible and readily available. Focusing on one of the most pressing topics facing present-day companies, the third stream provides a foundation for the academic conceptualization of data sourcing in the context of sustainability. The outcomes of this thesis project enable the transition from ad-hoc acquisition to well- informed, professional data sourcing approaches in the enterprise context. The contributions of the first research stream are an external data sourcing taxonomy (Essay 1), which informs sourcing decisions in an enterprise context, and a reference process to source and manage external data (Essay 2), which is accompanied by explicit prescriptions in the form of design principles. The second research stream proposes a use case-driven assessment of open corporate registers (Essay 3) and, building on the subsequent findings, a method to screen, assess, and prepare open data for use in support of companies’ open data activities (Essay 4). Finally, the third research stream reveals and elaborates on three data sourcing practices developed by companies in response to institutional pressures in the sustainability context (Essay 5)

Is Open Data Ready for Use by Enterprises? Learnings from Corporate Registers

Open data initiatives have long focused on motivating governmental bodies to open up their data. The number of open datasets is growing steadily, but their adoption is still lagging behind. An increasing number of studies assess open data portals and open data quality to shed light on open data’s current state. Since prior research addressed neither datasets’ content, nor whether it met enterprises’ data needs, our study aims to address this gap by investigating the extent to which open data is ready for use in the enterprise context. We focus on open corporate registers as an important segment of open government data with high relevance for enterprises. Our findings confirm that open datasets are heterogeneous in terms of access, licensing, and content, which makes them difficult to use in a business context. Our content analysis reveals that less than 50% of analyzed registers provide companies’ full legal addresses, while only 10% note their contact information. We conclude that open data in corporate registers has limited use to its lack of required attributes and relevant business concepts for typical use cases

Sourcing the Right Open Data: A Design Science Research Approach for the Enterprise Context

Open data has become increasingly attractive for users, especially companies, due to its value-creating capabilities and innovation potential. One essential challenge is to identify and leverage suitable open datasets that support specific business scenarios as well as strategic data goals. To overcome this challenge, companies need elaborate processes for open data sourcing. To this end, our research aims to develop prescriptive knowledge in the form of a meaningful method for screening, assessing, and preparing open data for use in an enterprise setting. In line with the principles of Action Design Research (ADR), we iteratively develop a method that comprises four phases and is enabled by knowledge graphs and linked data concepts. Our method supports companies in sourcing open data of uncertain data quality in a value-adding and demand-oriented manner, while creating more transparency about its content, licensing, and access conditions. From an academic perspective, our research conceptualizes open data sourcing as a purposeful and value-creating process

Toward Cross-Company Value Generation from Data: Design Principles for Developing and Operating Data Sharing Communities

Unlike other assets, data’s value increases when it is shared and reused. Whereas organizations have traditionally exchanged data vertically with other actors along the value chain, we observe that they increasingly share complementary data assets with others, even at times with their competitors, to address business and societal challenges. Research on these new forms of horizontal data sharing and the emerging data ecosystems is still scarce. Building on the theory of communities of practice, we study a pioneer data sharing community comprising more than 20 multinational companies that developed an innovative approach to pool data management efforts. We derive eight design principles for horizontal data sharing, which we cluster according to the following dimensions: domain of interest, shared practice, and community. By offering prescriptive design knowledge, our findings make an important contribution to the emerging literature on cross-company data sharing. Our research also provides practitioners with actionable insights on how to establish and operate data sharing communities effectively