Data management for sustainability regulations and standards Forty-Fifth International Conference on Information Systems, Bangkok, Thailand 2024 1 Towards Data Management for Navigating the Dynamic Landscape of Sustainability Regulations and Standards Completed Research Paper Jingyang Wang Faculty of Business Economics (HEC) University of Lausanne, Switzerland Jingyang.Wang@unil.ch Elizabeth A. Teracino Faculty of Business Economics (HEC) University of Lausanne, Switzerland elizabeth.teracino@unil.ch Christine Legner Faculty of Business Economics (HEC) University of Lausanne, Switzerland christine.legner@unil.ch Abstract Sustainability regulations and standards have proliferated for two decades, shifting from voluntary guidelines to mandatory compliance. The evolving and often ambiguous nature of data requirements derived from these regulations and standards pressure companies to adjust their data management practices. This article examines the dynamic landscape of sustainability regulations and standards through an institutional lens and categorizes them into four groups via the institutional pressures they catalyze. Based on 12 qualitative case studies we uncover key data management practices companies build in response to the institutional pressures. We thereby contribute a categorization of sustainability regulations and standards acting as catalysts of varying institutional pressures, and through emphasizing the data management practices companies develop in response, we bridge the current disconnect between sustainability studies and data management. Keywords: Data Management, Data Practices, Data Management Practices, Sustainability Regulations, Sustainability Standards, Institutional Theory, Data Perspective Introduction Sustainability is rapidly moving to the top of agendas across various sectors worldwide. The term sustainability integrates all three social, environmental and economic responsibilities (Gimenez et al., 2012), also known as the ‘triple bottom line’ (Milne & Gray, 2013). In 2015 the United Nations (UN) General Assembly broke these interlinked responsibilities down further by establishing 17 Sustainable Development Goals (SDGs) that emphasize the objectives that need to be addressed the year 2030 (United Nations, 2022). Since then, more and more sustainability regulations and standards have appeared to help enforce these world goals more tangibly, requiring more concrete actions from companies. A prominent example is the European Commission’s Corporate Sustainability Reporting Directive (CSRD), that requires companies to report on upwards of 1000 metrics in line with the European Sustainability Reporting Standards (ESRS). More than an estimated 50,000 companies will be directly impacted by CSRD’s reporting requirements, and those within the global supply chains of those directly impacted will be indirectly impacted as all disclosure-proofed inbound and outbound material and energy data flows from entire value chains and Data management for sustainability regulations and standards Forty-Fifth International Conference on Information Systems, Bangkok, Thailand 2024 2 product life cycles must be collected, analyzed, integrated and verified. Therefore, organizations, particularly multinationals, find themselves at a crossroads, compelled to navigate the intricate landscape of sustainability regulations and standards. The evolving and often ambiguous nature of data requirements derived from sustainability regulations and standards poses significant challenges for organizations. There are major hurdles in this context related to data management, as companies may not have collected the data, have access to it, or may not be able to ensure its quality (Krasikov & Legner, 2023). These include the complexities of collecting extensive data sets from heterogeneous sources and the technical hurdles of integrating disparate information systems (Zampou et al., 2022). Hence, though sustainability regulations and standards are not intrinsically about data, they require that organizations critically assess and adapt their data management to ensure compliance. This involves setting up new, or (re)adjusting existing, data management practices to address the growing data requirements necessary to adhere to regulations and standards. A systematic understanding of all the regulations and standards that companies must adhere to is lacking, and as they call for varying requirements on different levels and for different purposes, these thus require different data management responses from companies. Our research seeks to address the research question (RQ): RQ: How do companies develop data management practices to address the dynamic landscape of sustainability regulations and standards? Sustainability regulations serve as catalysts of varying forms of institutional pressures, as regulations and standards stem from varying pressure points – from top-down regulatory bodies demanding more transparency and disclosure to standards that address demands from consumers for product-level information for supply chain traceability in efforts to tackle consumer distrust (Biswas et al., 2023). In adjustment to the presence of these institutional pressures, companies adjust their data management by implementing new, and adjusting existing, practices in response. Institutional theory is therefore applied to our study as it serves as a lens for understanding how such institutional pressures shape these data management practices as organizational responses, particularly in revealing how companies manage, utilize, and report data for sustainability. Our research approach is two-fold: first we analyze the sustainability regulations and standards to categorize and map them via an institutional theory lens, and second, we conduct exploratory case studies with companies responding to sustainability regulations and standards to empirically identify the data management practices develop in response. Our key contribution is we were able to identify and observe six types of data management practices that companies developed as organizational responses to address four different categories of regulations and standards we found. Through emphasizing the data management practices required for addressing sustainability regulations and standards, we bridge the current disconnect between sustainability studies and data management. We thereby address the call for more research focused on a data perspective on sustainability (Aaltonen et al., 2023; Püchel et al., 2024). Practically, it offers companies actionable insights on how to evolve from reactive compliance to proactive sustainability through targeted data management approaches. practices. The structure of this paper is outlined as follows: In the theoretical background, we apply an institutional theory lens to sustainability regulations and standards, then introduce the data perspective for sustainability. The methodology section details our two-fold research process. We then present two primary findings: the categorization of sustainability regulations and standards, and the corresponding data management responses to each category. In the discussion and conclusion section, we explore how our findings lay a foundation for future research on sustainability by contributing a data perspective and provide practical guidance for organizations aiming to adapt their data management for navigating the dynamic sustainability regulatory landscape. Then the last section highlights the limitations of our study and discusses implications for future research. Theoretical Background Institutional Lens on Sustainability Regulations and Standards Institutional theory studies how organizations evolve by adopting widely recognized social structures, including schemas, rules, norms and routines from their surrounding environments, to maintain legitimacy Data management for sustainability regulations and standards Forty-Fifth International Conference on Information Systems, Bangkok, Thailand 2024 3 and ensure survival (DiMaggio & Powell, 1983; Friedland & Alford, 1991; Hirsch, 1975). Institutional theory has been proven fruitful for understanding the sustainability context, particularly the related institutional pressures stemming from shifts in social values, technological advancements, and regulatory changes shape organizational decisions (Ball & Craig, 2010; Rivera, 2004), as well as changes in management practices to address sustainability-related demands (Brown et al., 2006; Hoffman & Ventresca, 1999; Tate & Ellram, 2010). Institutional environments are shaped by a complex array of regulatory structures including governmental agencies, laws, judicial systems, professional standards, as well as societal and cultural practices that exert pressures for conformance (Suchman, 1995), and provide the incentivization structure of an economy (North, 1990). According to Scott (2008), there are three general dimensions of institutional pressures – regulative, normative and cultural-cognitive – that organizations must navigate to maintain good standing with their competitive environment. Regulative pressures typically come in the form of regulations, policies, and laws where a major source of enforcement mechanisms is governments (North, 1990; Scott, 2008). The laws and rules are adhered to avoid punitive consequences and to maintain legitimacy and garner benefits within their industry and/or greater environment (Scott, 2008). However, companies thus must not only satisfy the more obvious legal or formal requirements they face in the environment they subscribe to and operate in, but they also need to consider the broader expectations and values of the society in which they operate (Scott, 2008). In the studies of corporate sustainability, an extensive body of literature identified government regulations and standards as key drivers in stimulating changes of corporate management practices (Lozano, 2015; Rennings & Rammer, 2011). Regulatory pressures stemming from sustainability regulations and standards manifest as transparency and compliance demands (Butler, 2011; Yang, 2018). More specifically we see that sustainability regulations provide due diligence guidance or mandate corporate reporting obligations on clean and responsible production practices, for example, CSRD and the EU’s Supply Chain Act mandate supply chain due diligence for sustainable business operations. Regulatory bodies focused on human rights call for the continuous monitoring on issues related to corporate involvement in practices like child labor, for example, the Austria Human Right Due Diligence Act forces companies to examine their business efforts and impact on social factors. Given the broad impact scope and potentially disruptive consequences of global sustainability regulations and standards, organizations are under immense institutional pressures and must act in a responsible manner to stay compliant and upkeep their legitimacy. Sustainability-focused regulations and standards have existed for decades already and are there to help reshape the institutional environment that companies immerse themselves in. They form a dynamic landscape where some standards initially served primarily to raise public cognition but have evolved into widely accepted social norms or mandatory regulations over time. For instance, international standard development organizations like Global Reporting Initiatives (GRI) and the Sustainability Accounting Standards Board (SASB) encouraged the voluntary adoption of sustainability disclosure frameworks, thus igniting the public cognition of sustainability’s urgency (Nikolaeva & Bicho, 2011). Over time, old standards like GRI have not only raised awareness but also significantly shifted social norms within a decade of their introduction. The shift toward mandatory sustainability-focused legislation gained significant momentum with the introduction of the EU Green Deal in 2020, setting a precedent for global regulatory bodies to mandate measures that dictate companies’ activities and their sustainability impact across international value chains (European Commission, 2022). Such mandatory legislation is designed to bring out more regulative pressures for pushing forward the sustainability agenda. Meanwhile, the ultimate goals for some regulations and standards are transforming social norms steadily by embedding sustainability mindsets into the everyday operations of corporations. In a summary, various regulations and standards are formulated to exert different types of institutional pressures on companies. In such context, institutional theory offers a robust framework for understanding these dynamics by categorizing the distinct pressures exerted on organizations. Data Management for Sustainability Regulations and Standards Data plays a central role for companies to navigate sustainability regulations and standards. Companies cannot measure or report on whether they are complying to the regulations and standards without having data on many metrics. For example, the availability of high-quality data is essential for creating auditable sustainability reports as required by CSRD (Krasikov & Legner, 2023). Tracking material and product data Data management for sustainability regulations and standards Forty-Fifth International Conference on Information Systems, Bangkok, Thailand 2024 4 across the value chain and throughout the lifecycle is necessary to comply with the UK Plastic Tax (Cai & Waldmann, 2019). To this end, fulfilling regulatory requirements and effectively communicating companies’ sustainability efforts and impact rely on effective data management. Companies often struggle to meet externally imposed sustainability regulations within the required timelines, primarily due to significant issues with data availability, access, and quality (EDM Council, 2022). For example, non-financial reporting requires companies to collect, process, and interpret large amounts of data, e.g. on emissions and product compositions, often not systematically collected or analyzed prior. Even when data can be gathered there is still a reliance on estimates, compromising the reliability of the calculated sustainability indicators and creating a lack of trust in the data. The main Green Information Systems (Green IS) literature body has focused on systems for managing sustainability data, such as Environmental Management Information Systems (EMIS), and studies typically have focused on system design and implementation rather than on data itself (Bansal & Roth, 2000; El- Gayar & Fritz, 2006; Frost et al., 2012; Walls et al., 2011). While Green IS studies have repeatedly highlighted the data-oriented challenges, the academic literature does not explicitly address the subject, and a shared approach to data management is still lacking (Jarvenpaa & Markus, 2020). For example, Marx Gómez and Teuteberg (2015) and Zampou et al (2022) highlighted that data availability for a data integration should be considered when developing the EMIS. Watson et al. (2021) emphasized that an effectively designed EMIS must ensure data is collected with sufficient granularity. Without this detail, companies may discover data gaps when required to analyze material and component-level specifics. Addressing a part of this challenge, Guennoun et al. (2024) explored the sustainability data objectives and corresponding data life cycle management specific to SDG reporting, shedding light on the fact that traditional data management practices adopted in corporations often lacks the specificity to address the new and nuanced requirements and will need an overhaul to accommodate the growing sustainability data needs and requirements. However, their focus remains solely on SDGs, overlooking broader regulatory landscape. How to employ effective data management and solve these data gaps, and what are the key data management practices, are under-researched. To fill this gap, in this article we focus on the ever-evolving regulations and standards that serve as catalysts of institutional pressures and the responses they provoke via new and updated data management practices to contribute to the data perspective on sustainability. Methodology Research Design Considering the research objective, our study employs institutional theory to analyze the pressures stemming from sustainability regulations and standards. We additionally observe subsequent organizational responses in the form of data management practices. In consequence, our research comprise two phases as outlined in Table 1. We commenced with identifying, prioritizing, analyzing, and categorizing regulations and standards (Phase I) through the lens of Institutional Theory, and then progressed to qualitative methods including case studies (Van de Ven & Poole, 2005), to obtain empirical evidence of data management practices in response (Phase II). In the current dynamic landscape, multinationals face significant and direct institutional pressures from sustainability regulations and standards, as operating across regions and often in supply chains exacerbates the need to conform and maintain legitimacy by addressing compliance on both global and regional levels (Kostova et al., 2008). The actions of these larger companies are additionally more visible as they are in later stages of development, offering clearer examples of how organizations adapt to sustainability regulations and standards (Kostova et al., 2008). Studying the data management responses of multinationals, which bear more institutional pressures specifically catalyzed by sustainability regulations and standards, is done to provide more generalizable insights. Given this context, we collaborated with 12 multinational corporations within a multi-year research program exploring their data management practices for sustainability. All the companies involved in the study have been exposed to compliance pressures stemming from different sustainability regulations and standards. These companies, from highly institutionalized industries (Powell & DiMaggio, 2012), are subject to regulation, standardization, and formalization, necessitating compliance and adherence to institutional pressures to different extents. Targeting these compliance challenges, these purposefully sampled companies had already launched sustainability initiatives and were in the progress of refining their data management practices. Data management for sustainability regulations and standards Forty-Fifth International Conference on Information Systems, Bangkok, Thailand 2024 5 Our setting provided us with privileged access to interact with data experts closely and a vantage point to observe each company’s specific circumstances in detail and longitudinally. This enabled the research team to capture rich insights on how data management practices evolved across various levels of implementation and maturity, as companies were addressing sustainability regulations and standards. Data Collection Data Analysis Outcomes Phase I: Analyzing and categorizing regulations and standards (11.2022-08.2024) > 30 Sustainability Regulations, standards, initiatives, frameworks, etc. Content analyses according to the criteria (content focus, geographical application scope, industry application scope, disclosure type, disclosure obligations, disclosure granularity, auditing requirements, materiality standards, targeted stakeholders, targeted product category, involved supply chain activities, types of penalties), categorizing via institutional pressures regulations and standards were designed to evoke List and four categories of sustainability regulations and standards Phase II: Identifying data management practices through case studies (11.2022-08.2024) 12 x60-min interviews with 12 multinationals companies (individual semi-structured interviews with data experts) Corporate sustainability reports, etc. Within-case analyses: code each case as an independent entity to understand the data management practices employed by each interviewed company in response to specific regulations or standards Key data management practices in the companies developed in response to regulations or standards Focus groups with more than 30 participants from 12 multinationals (interdisciplinary practitioners, researchers, cross-industry discussions, and presentations) White papers, industrial reports, etc. Cross-case analyses: analyze commonalities among cases to derive organizational responses Four organizational responses Table 1. Research Design Phase I – Analyzing and Categorizing Regulations and Standards In Phase I, we undertook the screening of 30+ relevant sustainability regulations, standards, initiatives, frameworks, ensuring comprehensive coverage of all three dimensions of the triple bottom line. We then prioritized the most relevant ones in our study scope. We then analyzed these based on a set of criteria including content focus, geographic and industry application scope, type and obligations of disclosure, level of disclosure granularity, auditing requirements, materiality standards, targeted stakeholders, product categories, involved supply chain activities, and types of penalties. Our selected corpus of regulations includes the EU Green Deal, EU Taxonomy, Sustainable Finance Disclosure Regulation (SFDR), Corporate Sustainability Due Diligence Directive (CSDDD), Corporate Sustainability Reporting Directive (CSRD), Switzerland Ordinance on Climate Disclosures, UK Plastic Tax, German Supply Chain Due Diligence Act (LkSG), Eco-Design for Sustainable Products Regulation (ESPR), Austria Human Right Due Diligence Act, German Energy Efficiency Act (EnEfG), among others. Similarly, our list of standards features the International Financial Reporting Standards S1&S2 (IFRS S1&S2), German Association of the Automotive Industry (VDA) 284, International Sustainability and Carbon Certification (ISCC), Registration, Evaluation, Authorization and Restriction of Chemicals (REACH), European Federation of Pharmaceutical Industries and Associations (EFPIA) Disclosure Code, French Report on Greenhouse Gas Emissions (BEGES), and ISO/DIS 59000. Initiatives and frameworks studied include the Global Reporting Initiative (GRI) and the Task Force on Climate-related Financial Disclosures (TCFD). We included both existing and emerging sustainability regulations and standards in our analysis scope. Some standards, like GRI and TCFD, have been in place for decades, while others, such as ESPR, are still under development. Our aim is to gain an overarching view of this dynamic landscape, considering both its temporal depth and breadth. We found that regulators have designed sustainability regulations and standards with varying objectives— some focus on rigorous enforcement and scrutiny, while some others aim to advocate and facilitate sustainable practices across different sectors. Different regulations are thus intended to exert distinct types of institutional pressures on companies. Therefore, we systematically categorize them according to the institutional pressures they impose on organizations, helping to clarify the rationale behind their design. How companies respond, whether through genuine compliance or strategic gestures, is addressed in the second part of our study, where we examine data management practices multinationals develop in response to the dynamic sustainability regulatory landscape. Data management for sustainability regulations and standards Forty-Fifth International Conference on Information Systems, Bangkok, Thailand 2024 6 Phase II – Identifying Data Management Practices through Case Studies In Phase II, beyond cataloging these regulations and standards, we extended our analysis to data-centric examinations. This analysis provided us the sense that companies responded to different types of sustainability regulations and standards with different combinations of data sourcing, verification, and governance practices. Moreover, it indicated the varying scope of data and data-related projects that companies engaged in to comply with these regulatory requirements. Spanning the last two years, we employed 12 standalone case studies. Employing multiple case studies not only bolstered external validity but also facilitated analytical generalization (Yin, 2009). Importantly, the involved 12 multinational companies are all from highly institutionalized industries and at various stages of maturity in their sustainability efforts, confronting distinct challenges posed by a diverse array of sustainability regulations and standards. We collected primary data by conducting semi-structured interviews with key informants- respectively representing each of the 12 companies (see Table 2). We selected key informants who engage in ongoing sustainability initiatives and at the same time actively participate in managing and executing data-related activities, including sustainability data stewards responsible for managing the data, sustainability business analysts tasked with interpreting the data for business insights, and individuals involved in adjacent areas such as non-financial reporting. For each case, we conducted semi-structured interviews with the companies to garner insights about the sustainability regulations and standards they confront, their sustainability initiatives, underlying data requirements, and emerging data management practices (Castillo-Montoya, 2016). We additionally conducted an in-depth review of over a thousand pages of corporate sustainability reports (issued by the companies we collaborated with in this phase) and industrial white papers, including those from consulting firms and NGOs. As part of case analysis, we identify data management practices and documented the sustainability regulations and standards that posed the most significant burden on the companies. This also empirically validated our list and categorization of regulations curated in Phase I. We documented the corresponding data management challenges they encountered, sustainability-relevant data projects, involved roles, and key data management practices emerging in these companies. We mapped these data management practices along the 5C’s of Data Management from the MISQ research curation (Chua et al., 2022), which comprise comprehensively the activities and methods that define the data management field: conceptualize, collect, curate, consume, and control data. Through this mapping, we link key sustainability regulations and the responsive data management practices for each case. Drawing on these insights, we were able to find patterns in their responses to each category of sustainability regulations and standards, thereby were able to generalize and structure our findings. We additionally conducted 9 focus groups consisting of more than 30 participants from the involved multinationals, where both sustainability and data management experts from each company were present. We were able to triangulate our findings in these subsequent steps. Data management for sustainability regulations and standards Forty-Fifth International Conference on Information Systems, Bangkok, Thailand 2024 7 Results Landscape of Sustainability Regulations and Standards As previously discussed, we view sustainability regulations and standards as catalysts of institutional pressures in this study. To understand the dynamic landscape of sustainability regulations and standards, we position them according to the institutional pressures they catalyze and impose on organizations (see Figure 1). The vertical axis of Figure 1 arranges sustainability regulations and standards according to the level of regulative pressure they exert, ranging from lower to higher. This escalation of regulative pressure can be understood as a transition from voluntary guidelines to mandatory compliance, which we have seen manifest in the context of sustainability over recent decades quite prominently. The horizontal axis depicts a shift from cognitive pressure to normative pressure. Sustainability regulations and standards that exert cognitive pressures often originate from emerging societal and market expectations that influence corporate cognition and behaviors (Scott, 2008). For instance, this would look like companies gradually recognizing that consumers are beginning to prefer environmentally friendly products, and perhaps they observe that some competitors are securing certifications for their products, such as ecolabels benchmarked by the industrial leaders, to market to the new customer preferences. Driven by such cognitive pressures, companies mimetically react by imitating some successful sustainability management practices to mitigate uncertainty and delivering a “green image” to the public (Melville, 2010). Moving towards the normative end of the axis, as sustainability awareness within organizations becomes more mature, societal expectations and norms become more formalized and prominent, e.g., through norms, protocols, and value systems (Teracino, 2017). In contrast to cognitive pressures, normative pressures require weaving sustainable practices into the very culture and identity of the organization, whereby it is not merely a trend, but becomes a culture prevalently accepted and expected across various social groups (Scott, 2013). For instance, companies find their practices under social or cultural expectations from multiple stakeholders: customers are expecting that clean and responsible management considerations are integrated into daily supply chain practices; investors are making critical investment decisions based on the disclosed sustainable practices; or customer perception towards sustainability in the market is changing in an extensive way. These prompt companies to reevaluate and realign their value propositions in response to these evolving expectations (Lu et al., 2018). By positioning the identified sustainability regulations and standards along these two axes, whereby they are categorized via institutional Cases Key Informants Key Regulations/Standards A - Retail ($1B–50B/~15,000) Lead Global sustainability data CSRD, CSDDD, EU Taxonomy B - Software development ($1B– 50B/~100,000) Manager product & service and data analyst CSRD, EU Taxonomy, IFRS S1&S2, EnEfG C - Insurance ($50B–100B/~150,000) Lead Digital sustainability and senior data architects CSRD, EU Taxonomy, SFDR, BEGES D - Manufacturing, medical ($1B– 50B/~60,000) Director data engineering and sustainability steward CSRD, EU Taxonomy, LkSG, IFRS S1&S2 E- Engineering and electronics ($50B– 100B/~400,000) Director master data management CSRD, EU Taxonomy, LkSG, UK Plastic Tax, ISCC F - Consumer goods ($50B–100B/~350,000) Global master data lead CSRD, EU Taxonomy, LkSG, UK Plastic Tax, B Corp G - Pharmaceutical, Chemicals ($1B– $50B/~100,000) Head of product data management CSRD, EU Taxonomy, LkSG, Switzerland Ordinance on Climate Disclosures, REACH, EFPIA Disclosure Code H - Logistics ($1B–$50B/~70,000) Program manager governance CSRD, EU Taxonomy, LkSG, I - Manufacturing, automotive ($1B– $50B/~90,000) Senior data and analytics governance professional CSRD, EU Taxonomy, LkSG, ISCC, Anti- Waste Law J - Adhesive & beauty products manufacturing ($1B–$50B/~20,000) Director master data CSRD, EU Taxonomy, LkSG, UK Plastic Tax K - Packaging, food processing ($1B– $50B/~25, 000) Enterprise data governance manager CSRD, EU Taxonomy, LkSG, UK Plastic Tax, EU Ecolabel L - Manufacturing, automotive ($1B– $50B/~150, 000) Senior data architect CSRD, EU Taxonomy, ESPR, VDA 284 Table 2. Case Descriptions Data management for sustainability regulations and standards Forty-Fifth International Conference on Information Systems, Bangkok, Thailand 2024 8 pressures regulations and standards were designed to evoke, we can identify four main categories: Certificate, Due Diligence, Reporting, and Eco-design. Figure 1. Sustainability Regulations and Standards Categorized by Institutional Pressures The first category C-1 is labeled as Certificate. This category highlights companies’ cognition of changing sustainability demands and the onset of cognitive pressures. Typically, the sustainability regulations and standards falling into this category are voluntary, which means these comprise lower amounts of regulative pressures. This category captures the phase of cognizance, where companies start to internalize the concept of sustainability as they seek to acquire certificates or licenses for marking their sustainability achievements to begin to signify that they are sustainable to their customer base. For example, companies recognize that costumers are inclined to buy more products that are eco-friendly or socially responsible. Through product labeling, such as B Corp certification or the EU Ecolabel, they can potentially incentivize consumer purchases by signaling a commitment to these values. Or they notice that competitors have secured International Sustainability and Carbon Certification (ISCC) certifications for their manufacturing platforms, they may decide to consider this as a possibility for themselves and follow suit. There is an awareness that broadcasting these credentials of their participation in sustainability commitments could potentially secure their shareholders and mitigate risks. The second category C-2 is labeled as Due Diligence. Sustainability regulations and standards falling into this category still function mainly on exerting cognitive pressure, by raising awareness and cognition surrounding the importance of sustainability topics, but the regulative pressure is leveled up – these mandate companies to uphold sustainability commitments through examination on compliance. Due diligence acts and tax regulations that impose mandatory implementation of due diligence measures belong to this category. For instance, the Supply Chain Due Diligence Acts, like the German Supply Chain Due Diligence Act (LkSG), require companies within their jurisdiction to meticulously review their supply chain operations, ensuring responsible practices that align with international sustainability objectives. Such measures enforce accountability through a set of principles and procedures dedicated to monitoring, assessing, and validating companies’ environmental and social commitments. Under the auspices of these regulations, organizations must engage in thorough internal and external scrutiny - often via comprehensive audits and verification assessments, where a regulatory body is involved. Thus, here it is not just about identifying and mitigating risks associated with environmental and social factors via cognitive pressures, but also about understanding the impact of their operations on society and the environment to address an additional regulative pressure. As a result, the elevated regulative pressure triggers companies engaged with a deeper level sustainability engagement from a verification standpoint. Data management for sustainability regulations and standards Forty-Fifth International Conference on Information Systems, Bangkok, Thailand 2024 9 The third category C-3 is labeled as Reporting. This category contains sustainability regulations and standards that still exert a high level of regulative pressure, but shifts focus from cognitive to normative pressure - where mere internal cognition is insufficient, as the breadth of scope is far larger, and disclosure to parties outside of the organization is required. Reporting regulations and standards such as CSRD and EU Taxonomy mandate sustainability information disclosure obligations belong to this category. Here, the demands for extensive information disclosure push companies to reflect and reassess their entire value chains, including not only their own operations, but operations of upstream suppliers and downstream customer activities. According to the Director data engineering from Company D, fulfilling some requirements mandated by CSRD is like “mission impossible.” Mere reflection or reassessment cannot help companies to achieve compliance, because if some of the required information, such as downstream customer activities, have never previously existed, companies find themselves in a position where they cannot simply gather or create these out of thin air. Under this category, “ex-post” examination is not sufficient, a backward step to reconsider grounded-up practices, or a transition to “ex-ante” intervention, could be necessary (Argus et al., 2020). The fourth category C-4 is labeled as Eco-Design. This category highlights the predominance of normative pressure coupled with the relatively low-level of regulative pressure. Here, normative pressure is the main driver of organizational response, suggesting that even with less strict compliance requirements, companies still choose to adopt sustainability practices voluntarily, driven by social or cultural norms rather than strict regulatory requirements. Sustainability regulations and standards within this category are less about monitoring but more about empowerment, aiming to facilitate companies lay groundwork for environmental and social development. For example, a regulation like Eco-design for Sustainable Products Regulation (ESPR) drives companies back to the product design phase and advocates for the integration of sustainability considerations from the earliest stages of product conceptualization. At least until now, the antecedents of ESPR and ESPR itself are not bundled with strict regulative pressures. Instead, companies embrace such regulations because of the shift in consumer and investor expectations towards sustainability. To gain a competitive edge and align with these changes in social and cultural behaviors stemming from normative pressures, they need to go back to the drawing board- this time, with a fresh perspective that calls for a comprehensive redesign of products, operational processes, and supply chains, embedding sustainability at the core of their business model. Organizational Responses and Data Management Practices Through the case studies, we gained insights into the data management practices that companies develop as organizational responses to the sustainability regulations and standards faced by companies. Data management is expected to have an idiosyncratic flavor as these are intended as the bridge between data management practices and the overarching organizational strategy they aim to match and/or achieve. Data management practices refer to the specific activities and processes that organizations engage in to handle, process, and utilize data effectively within their operations (Cook & Brown, 1999). These practices encompass a wide range of tasks that are critical to ensuring that data is accurately curated, stored, maintained, and made accessible for various organizational needs. Chua et al. (2022) identify 5 key types of data management practices (MISQ 5C’s of Data Management): data conceptualization, data collection, data curation, data consumption, and data control, which we adopt in this study. The organizational responses of the companies refer to the overarching configurations of these practices adopted to achieve organizational goals. We derived those from the cross-case analysis and summarize them in Table 3. The first organizational response that organizations utilize to respond to sustainability regulations and standards within the Certificate category, we identify as “signaling sustainability achievements” (S-1: Signal). Companies engage in signaling to their stakeholders by creating and controlling one-time-use data that portray compliance and compile them to acquire certificates. For example, company E aimed to acquire an ISCC certification for their manufacturing platforms and company F aimed to acquire B corp certificate for labeling itself as a “green” company. Those companies gathered required data from relevant sources, including self-reported supplier questionnaires. Upon collecting this data, they proceeded to validate and issue information such as Environmental Product Declarations to the public. This response’s key feature lies in its ad-hoc data management practices, which contribute to crafting an image or signal that aligns with market and regulatory expectations, often serving more as a symbolic gesture of commitment rather than indicative of substantive change. Data management for sustainability regulations and standards Forty-Fifth International Conference on Information Systems, Bangkok, Thailand 2024 10 Catalysts Responses Key Data Management Practices (organized along the MISQ 5C’s of Data Management) Exemplary Responses Exemplary Cases C-1: Certificate S-1: Signal Signal sustainability achievements Collect: - Ad-hoc data collection Control: - Ad-hoc data verification Collect one-time-use data profiles for specific purpose: - Environmental product declarations - Questionnaires for suppliers E - Engineering and electronics (ISCC); F - Consumer goods ( B Corp); G - Pharmaceutical, Chemicals (REACH); C-2: Due Diligence S-2: Comply Comply to strict regulatory obligations with practical feasibility Collect & Curate: - Routine-based data collection Control: - Routine-based data verification Set up standards and guidelines: - Corporate responsibility self- assessment forms - Supply chain code of conduct Streamline routine-based data management practices: - Sustainability data catalog B - Software development (EnEfG); C – Insurance (BEGES); I - Manufacturing, automotive (LkSG) J - Adhesive & beauty products manufacturing (UK Plastic Tax); K - Packaging, food processing (LkSG) C-3: Reporting S-3: Systematize Systematize working flows via communicating sustainability commitments Conceptualize: - Sustainability data lifecycle management Collect & Curate: - Data sourcing along supply chains Control: - Internal compliance risk control - Strict quality assurance process Consume: - Analytics and dashboards Set up standardization working groups for sustainability: - Identify data interoperability - Standardize semantic layers of data modeling - Standardize calculation measures of reporting metrics and targets - Standardize roles and responsibilities - Standardize quality assurance guidelines Set up intraorganizational data sharing channels: - Sustainability data marketplace A - Retail (CSRD, CSDDD, EU Taxonomy); C - Insurance (SFDR); D - Manufacturing, medical (CSRD); I - Manufacturing, automotive (CSRD, EU Taxonomy) C-4: Eco- Design S-4: Cultivate Cultivate sustainability culture Conceptualize: - Sustainability data lifecycle management Collect & Curate - Data sourcing along supply chains Control: - Routine-based data verification Consume: - Analytics and dashboards Cross-Share (found as additional data practice in this study): - Interorganizational data sharing & governance - Stakeholder relationship management Launch sustainability by design initiatives: - Adopt eco & circular design principles - Adopt eco & circular design information systems - Initiate green supply chain programs Initiate interorganizational data sharing channels: - Digital Product Passports - Data spaces - Crowdsourcing platform Engage in dialogues with regulators: - Data stewardship in global forums for green transitions C – Insurance (ESPR); L - Manufacturing, automotive (ESPR, VDA 284) Table 3. Data Management Responses for Sustainability The second response falls within the Due Diligence category, which we identify as an organizational response for complying strict regulatory obligations with practical feasibility (S-2: Comply). In contrast to S-1, S-2 requires organizations burdened with due diligence obligations establishing more routine-based data management practices. This involves adapting internal data management practices to meet these external demands while seeking compromises where necessary. For example, in response to the German Supply Chain Due Diligence Act (LkSG), company K implemented corporate responsibility self-assessment forms for the purpose of internal compliance risk control. Furthermore, they govern and evaluate suppliers’ conduct through the Supply Chain Code of Conduct. The balance is struck between strict compliance and Data management for sustainability regulations and standards Forty-Fifth International Conference on Information Systems, Bangkok, Thailand 2024 11 practical feasibility, aiming to find a sustainable middle ground that satisfies regulatory requirements and organizational capacities. Typically, companies respond with a reactive gesture by developing sustainability data management standards and pipelines, but – compared to S-1 – dedicated routines for data sourcing and verification. The third response falls within the Reporting category, which we uncover as an organizational response for systematizing complex processes and communications (S-3: Systematize). This involves rationalizing and streamlining the flow of information and practices within the organization to reduce chaos and increase efficiency. To respond to Reporting regulations and standards, one of the significant challenges firms encounter in their sustainability efforts relates to not only their own business operations but the activities of their supply chain or third-party partners, where the scope is broad and there are a variety of new information requirements spanning all three areas of Environmental, Social and Governance. More specifically, they develop data management practices for sourcing of data across supply chains and sharing cross-functional data internally, serving different internal sustainability information disclosure functions, such as reporting or risk monitoring. Faced with multifarious stakeholders, ranging from external parties such as investors, suppliers, recyclers, and regulators to internal departmental groups spanning data, finance, legal, procurement, production, and marketing departments, the primary challenge that emerges for companies is communicating data requirements with unclear setting of roles and responsibilities. Companies respond to normative pressures and additional high level of regulative pressures by initiating transformations of their data management practices in a more comprehensive manner. To set up ruling orders to manage the multi-faceted stakeholder communication complexity, a common approach we saw in our cases involved the establishment of standardization working groups (also known as specializing groups) dedicated to sustainability. For instance, company K has established a sustainability topic-specific steering group that includes representatives from all divisions, functions, and regions. This group assesses and prioritizes urgent sustainability topics during monthly information-sharing meetings. Additionally, their Sustainability Compliance & Risk Committee oversees the process. They ensure clear definitions of responsibilities, create a uniform view of cross-functional risks through consistent measurement and prioritization methods, and develop and monitor risk mitigation activities. These overarching and horizontal groups play a critical role in identifying data interoperability issues across various reporting regulations and standards to minimize redundant efforts. Also, they are tasked with standardizing the semantic layers of data modeling to ensure a unified understanding of reporting metrics and targets within the organization, as well as the measurement methods for these metrics to ensure that employees from different functional units adhere to a consistent set of standards. This facilitates the smooth integration of data. Such groups also standardize roles and responsibilities, clarifying individual duties and fostering accountability for progress. Moreover, they establish quality assurance guidelines, such as data quality protocols, to mitigate non-compliance risks. Effectively, these working groups, which ought to be cross- functional, are instrumental in driving inter-organizational communication, so that stakeholders are well- communicated and aligned with the compliance objectives. In addition to establishing standardization working groups to standardize the data management practices and routines, companies also respond by developing intraorganizational data sharing platforms. A notable example from our case studies is Company A – they launched an internal sustainability marketplace. Typically, in a data marketplace, the tangled web of interactions among various roles is streamlined into a simple, manageable, two-dimensional networks. This network connects data consumers—who request and specify what data and analytics they need—with data producers—who are tasked with creating these data and analytics and those are listed in the open marketplace for potential reuses. This marketplace approach facilities the response to all cognitive, normative, and regulative pressures in the context of sustainability regulation compliance. It first elevates awareness within organizations about the necessity of making sustainability data and analytics available more broadly, and treating data through curation for the marketplace becomes a standard practice within the organization over time, in normative fashion. It also provides flexibility for targeting different forms of regulations ensuring that data handling meets regulatory requirements through standardized, repeatable processes. This approach shifts data management practices from ad hoc efforts to consistent and systematized data management. The fourth response falls within the Eco-Design category, which we uncover as an organizational response for integrating sustainability into the core business operations (S-4: Cultivate). To respond to Eco-Design regulations and standards, organizations looked beyond mere compliance to evolve their practices, cultures, Data management for sustainability regulations and standards Forty-Fifth International Conference on Information Systems, Bangkok, Thailand 2024 12 and behaviors, driving long-term changes that transcends sustainability to become a core driver of organizational strategy, innovation, and competitive advantage. Fulfilling the Eco-Design regulations and standards requires a proactive forward-thinking, and value-creating response, which fundamentally redefines an organization’s value proposition and create a new identity for it (Neergaard & Pedersen, 2017). The shift towards Eco-Design signifies a value proposition shift from symbolic gestures towards more substantial sustainability contributions, driving data-driven innovation development that scale across business lines and functions around sustainability. We identified key data management practices for Eco-Design through our analysis of companies that have launched sustainability transformation initiatives. We found that while S-3 focuses on sourcing and leveraging existing data in a systematized manner, S-4 distinguishes itself by fundamentally embedding sustainability into the creation of new data flows - creating new data streams that are inherently for sustainability purpose. This action is characterized by launching sustainability initiatives that incorporate eco-design and circular economy principles directly into product design processes (Diaz et al., 2021). These initiatives are supported by the deployment of new information systems that serve as foundational infrastructures, enabling the generation of new, sustainability-driven data from the ground up. For example, company L launched a new product design system integrating both digital twin and artificial intelligence technologies for integrating sustainability considerations into their product R&D phase and company B, C, and G are actively evolving their sustainability practices by either physical or digital product design and Company C as an pioneering insurance company who owns only digital products even redesign their enterprise architecture. Moreover, a notable difference from the S-3 response is data utilization. While companies operating under the S-3 response developed data product marketplaces for internal communication of sustainability objectives, those embracing the S-4 response have extended their efforts to establish interorganizational data-sharing platforms. Solutions like Digital Product Passports, public data spaces (e.g., Catena-X), and crowdsourcing platforms are also employed by company L to reinforce the commitment to eco-design and circularity principles facilitate broader stakeholder engagement and collaboration in sustainability initiatives. Such interorganizational data sharing platform allows the exchange of data assets and capabilities, enabling organizations to access complementary resources they previously lacked (Wixom et al., 2020). Overall, our findings show that companies vary their data management responses depending on the different pressures stemming from the regulations and standards as per their categorization. Interestingly, those responses can even reshape the sources of these institutional pressures themselves, which are sustainability regulations and standards in this study. We observed that some data experts (e.g., from company C) participated actively in green transition forums like the Carbon Pricing Leadership Coalition and the International Alliance on Climate, leveraging their insights and data management practices to influence the shaping of regulations and standards themselves. By engaging in these dialogues, organizations contribute their on-the-ground experiences and data-driven evidence to support the creation of achievable policies. In sum, companies leverage their data not merely as a means of a passive response to the institutional pressures derived from sustainability regulations and standards but as a proactive instrument for lobbying, aiming to influence and reshape the evolving landscape of sustainability regulations and standards, ensuring they are grounded in real-world data management practices and tangible sustainability outcomes. Discussion and Conclusion The existing literature in IS focused on sustainability had thus far primarily focused on the design and adoption of EMIS or social-technical systems through which organizations become socially and environmentally responsible, particularly emphasizing the transformative potential of green IS (Krasikov & Legner, 2023; Püchel et al., 2024). However, a data perspective was largely missing (Krasikov & Legner, 2023), despite the relevance of data for addressing sustainability challenges and increasing pressure from sustainability regulations and standards. To address this research gap, our study analyzed and categorized sustainability regulations and standards and mapped these according to the institutional pressures they catalyze and impose on organizations, resulting in four groupings: Certificate, Due Diligence, Reporting, Eco-Design. Our findings were then explored through qualitative case studies with 12 companies’ data management practices developed in response to each category: Signify, Comply, Systematize, Cultivate. Data management for sustainability regulations and standards Forty-Fifth International Conference on Information Systems, Bangkok, Thailand 2024 13 From these we were able to derive a data management response framework for responding to sustainability regulations and standards. Our findings therefore extend IS research on sustainability and contribute to developing a dedicated data perspective on sustainability (Aaltonen et al., 2023), by shedding empirical light on the key data management practices needed for addressing an ever-changing landscape of sustainability regulations and standards. Interestingly we noticed that the data management responses became increasingly more proactive as pressures shifted from cognitive to normative, and the regulative pressure went from voluntary to mandatory. Companies typically initiated their sustainability journey by perceiving cognitive pressure and were able to respond with voluntary compliance, normally starting with passive “Signal” responses, such as acquiring labels or certificates. Then, as firms experienced a higher level of regulative pressure, as the mandatory nature of the regulations or standards became more denounced, they advance to the “Comply” responses. This response is characterized by an increased routine-based data management practices to enable firms to handle sustainability data and disclosure requirements so that they can pass through the monitoring and auditing processes reactively. As normative pressures increased, companies demonstrated more proactive responses, urging companies to adopt broader social and cultural norms, and advancing to the “Systematize” and “Cultivate” responses. While the rational progression would ideally move from “Systematize” to “Cultivate,” companies that preemptively engage with “Eco-Design” regulations and standards through value-creative and innovation-driven responses may find themselves well-prepared to manage the “Reporting” demands. Such proactive engagement ensures that all data necessary for sustainability reporting has already existed and is easy to curate and manage. One of the commonly observed data management trajectories we observed in our case studies was that, initially, firms became familiar with sustainability through Certificates and Eco-design principles established in the last century. For many years, both their compliance and information disclosure were voluntary, often symbolically employing reporting frameworks like GRI to showcase their risk management efforts and signal their commitments rather than effecting substantial environmental or societal impacts (Hahn & Lülfs, 2014). However, with the introduction of CSRD, companies now are overwhelmed by confronting sustainability Reporting regulations that mandate thorough and extensive sustainability reporting. Symbolic gestures are no longer sufficient as stricter regulatory bodies require more significant contributions. Companies pre-invested in Eco-design and Due Diligence could find themselves better prepared for these rigorous reporting standards. Of course, this evolution in sustainability practices is also intertwined with the digital technological revolutions of recent decades. Our findings also contribute to improving our understanding of key data management practices for sustainability along the 5C’s of Data Management from the recent MISQ research curation (Chua et al., 2022). We thereby connect the evolving and far-reaching data requirements from sustainability regulations and standards to established data management practices. Our findings also provide evidence for an additional practice – cross-share – which emphasizes interorganizational information flows and sharing, bringing the 5Cs to 6Cs. In applying the 5Cs during the case studies, we were able to follow how they develop and mature over time in response to institutional pressures. Our study indicates that when organizations are faced with normative pressures that surpass regulative ones, they are more likely to undertake radical and substantive transformations to their data management practices. This dynamic repositioning not only aligns with regulatory expectations but also catalyzes further innovation. Our study resonates with the study of Ball and Craig (2010), which also pointed out that normative pressures significantly drive sustainability awareness among organizations, and they advocate for more research on applying institutional theory on sustainability topics, to understand the new social norms derived from sustainability—such as ethical values and ecological consciousness—and how organizations react to them. Our research findings are additionally highly relevant for practitioners, especially in the era when organizations are facing technological advancements and regulatory changes in context of sustainability. The results of this study offer companies a framework by which to strategically evaluate which institutional pressures they are facing by way of prioritized sustainability regulations and standards and assess and improve upon their own data management practices in response, so that they may avoid non-compliance and shift towards becoming sustainable substantively. Companies can first map out which pressures they are more susceptible to, by identifying which regulations and standards are being prioritized at their company as well as which will influence their offerings if not already. Then companies can self-assess where they stand regarding key data management practices, to see if these align or if proactive adjustments can Data management for sustainability regulations and standards Forty-Fifth International Conference on Information Systems, Bangkok, Thailand 2024 14 be made. Matching their data management practices with the pressures they are facing can help them to self-assess whether they are responding to, or are set up to respond to, the pressures they face, and can push them to address more normative pressures proactively. Limitations and Future Work Limitations of this study include that the different categories are partially overlapping and that a very clear cut between the categories is difficult, particularly when accounting for the dynamism of the regulations and standards landscape. Moreover, organizations find themselves are often simultaneously influenced by institutional pressures driven by a diverse array of sustainability regulations and standards. This multiplicity adds a layer of complexity to their strategic responses, suggesting that the pathway to sustainability is not straightforward but rather a multifaceted journey marked by continuous adaptation and recalibration of strategies. These companies with different sizes, digitalization levels, and sustainability legacy may respond differently to institutional pressures compared to multinationals, potentially offering unique insights into their data management. Therefore, a promising direction for future research would be to include a broader sample of multinationals and examine SMEs and born-green companies in comparative study. This could help identify contingencies in response to sustainability regulations and standards with data management practices. Our investigation also reveals that the adoption of data management practices tailored to the dynamic landscape of sustainability regulations and standards does not inherently guarantee improved organizational performance. The success of data management depends on several factors, including the organization’s pre-existing data management practices, how well sustainability goals are integrated with the overall business strategy, and the influence of the external environment. The literature suggests that the external environment can significantly influence the degree to which companies need to and are able to adapt their management practices (Hanelt et al., 2021). Therefore, data management practices should be viewed as part of a comprehensive, integrated approach to sustainability that includes organizational change management, stakeholder engagement, and ongoing learning and adaptation. The sustainability context provides an excellent backdrop from which to view and trace how companies build and mature their data management practices. Relatedly, future research could explore the trajectories companies take in building up their data management in response to sustainability regulations and standards. In this vein, cross-company sustainability data sharing and how companies navigate coopetition scenarios would be fruitful for future research as well (Leidner et al., 2022). Such studies would provide deeper insights into the competitive and collaborative dynamics that shape the implementation of data management strategies for sustainability in complex business environments. Acknowledgements This work was supported by the Competence Center Corporate Data Quality (CC CDQ). The authors would like to thank all CC CDQ partner companies for their financial support, and their active contributions to this research. References Aaltonen, A., Alaimo, C., Parmiggiani, E., Stelmaszak, M., Jarvenpaa, S., Kallinikos, J., & Monteiro, E. (2023). What is Missing from Research on Data in Information Systems? Insights from the Inaugural Workshop on Data Research. Communications of the Association for Information Systems, 53(1), 4– 19. Argus, K., Iyer-Raniga, U., Bueren, B. V., & Mulherin, P. (2020). Data quality as an antecedent for commercial viability of circular economy business models: A case study. IOP Conference Series: Earth and Environmental Science, 588, 022059. Ball, A., & Craig, R. (2010). Using neo-institutionalism to advance social and environmental accounting. Critical Perspectives on Accounting, 21(4), 283–293. Bansal, P., & Roth, K. (2000). Why Companies Go Green: A Model of Ecological Responsiveness. The Academy of Management Journal, 43(4), 717–736. Data management for sustainability regulations and standards Forty-Fifth International Conference on Information Systems, Bangkok, Thailand 2024 15 Biswas, D., Jalali, H., Ansaripoor, A. H., & Giovanni, P. D. (2023). Traceability vs. Sustainability in supply chains: The implications of blockchain. European Journal of Operational Research, 305(1), 128–147. Brown, T. J., Dacin, P. A., Pratt, M. G., & Whetten, D. A. (2006). Identity, intended image, construed image, and reputation: An interdisciplinary framework and suggested terminology. Journal of the Academy of Marketing Science, 34(2), 99–106. Butler, T. (2011). Compliance with Institutional Imperatives on Environmental Sustainability: Building Theory on the Role of Green IS. The Journal of Strategic Information Systems, 20(1), 6–26. Cai, G., & Waldmann, D. (2019). A material and component bank to facilitate material recycling and component reuse for a sustainable construction: Concept and preliminary study. Clean Technologies and Environmental Policy, 21, 2015–2032. Castillo-Montoya, M. (2016). Preparing for Interview Research: The Interview Protocol Refinement Framework. The Qualitative Report, 21(5), 811–831. Chua, C., Indulska, M., Lukyanenko, R., Maass, W., & Storey, V. C. (2022, February 14). MISQ Research Curation on Data Management. MIS Quarterly. Cook, S. D., & Brown, J. S. (1999). Bridging epistemologies: The generative dance between organizational knowledge and organizational knowing. Organization Science, 10(4), 381–400. Diaz, A., Schöggl, J.-P., Reyes, T., & Baumgartner, R. J. (2021). Sustainable product development in a circular economy: Implications for products, actors, decision-making support and lifecycle information management. Sustainable Production and Consumption, 26, 1031–1045. DiMaggio, P. J., & Powell, W. W. (1983). The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields Paul J. DiMaggio; Walter W. Powell. American Sociological Review, 48(2), 147–160. EDM Council. (2022). ESG Data Management: Asset Owners. https://edmcouncil.org/groups-leadership- forums/esg-data-management/ El-Gayar, O., & Fritz, B. D. (2006). Environmental Management Information Systems (EMIS) for Sustainable Development: A Conceptual Overview. Communications of the Association for Information Systems, 17(1), 756–784. European Commission. (2022). A European Green Deal. European Commission - European Commission. https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en Friedland, R., & Alford, R. R. (1991). The New Institutionalism in Organizational Analysis. In Bringing Society Back In: Symbols, Practices, and Institutional Contradictions (pp. 232–266). University of Chicago Press. Frost, G., Jones, S., & Lee, P. (2012). The Measurement and Reporting of Sustainability Information within the Organization: A Case Analysis. Emerald Group Publishing. Gimenez, C., Sierra, V., & Rodon, J. (2012). Sustainable operations: Their impact on the triple bottom line. International Journal of Production Economics, 140(1), 149–159. Guennoun, R., Winkelmann, S., & Möller, F. (2024). Data for Sustainable Development in Logistics and Supply Chains—A Systematic Literature Review. Proceedings of the 57th Hawaii International Conference on System Sciences (HICSS). Hahn, R., & Lülfs, R. (2014). Legitimizing negative aspects in GRI-oriented sustainability reporting: A qualitative analysis of corporate disclosure strategies. Journal of Business Ethics, 123, 401–420. Hanelt, A., Bohnsack, R., Marz, D., & Antunes Marante, C. (2021). A systematic review of the literature on digital transformation: Insights and implications for strategy and organizational change. Journal of Management Studies, 58(5), 1159–1197. Hirsch, P. M. (1975). Organizational effectiveness and the institutional environment. Administrative Science Quarterly, 327–344. Hoffman, A. J., & Ventresca, M. J. (1999). The institutional framing of policy debates: Economics versus the environment. American Behavioral Scientist, 42(8), 1368–1392. Jarvenpaa, S. L., & Markus, M. L. (2020). Data Sourcing and Data Partnerships: Opportunities for IS Sourcing Research. In Information Systems Outsourcing (5th ed., pp. 61–79). Springer. Kostova, T., Roth, K., & Dacin, M. T. (2008). Institutional Theory in the Study of Multinational Corporations: A Critique and New Directions. Academy of Management Review, 33(4), 994–1006. Krasikov, P., & Legner, C. (2023). Introducing a Data Perspective to Sustainability: How Companies Develop Data Sourcing Practices for Sustainability Initiatives. Communications of the Association for Information Systems. Communications of the AIS, 53(1), 162–188. Leidner, D. E., Sutanto, J., & Goutas, L. (2022). Multifarious Roles and Conflicts on an Interorganizational Green IS. MIS Quarterly, 46(1), 591–608. https://doi.org/10.25300/MISQ/2022/15116 Data management for sustainability regulations and standards Forty-Fifth International Conference on Information Systems, Bangkok, Thailand 2024 16 Lozano, R. (2015). A holistic perspective on corporate sustainability drivers. Corporate Social Responsibility and Environmental Management, 22(1), 32–44. Lu, Y., Zhao, C., Xu, L., & Shen, L. (2018). Dual Institutional Pressures, Sustainable Supply Chain Practice and Performance Outcome. Sustainability, 10(9), 3247. Marx Gómez, J., & Teuteberg, F. (2015). Toward the Next Generation of Corporate Environmental Management Information Systems: What is Still Missing? In L. M. Hilty & B. Aebischer (Eds.), ICT Innovations for Sustainability (pp. 313–332). Springer International Publishing. Melville. (2010). Information Systems Innovation for Environmental Sustainability. MIS Quarterly, 34(1), 1. https://doi.org/10.2307/20721412 Milne, M. J., & Gray, R. (2013). W (h) ither Ecology? The Triple Bottom Line, the Global Reporting Initiative, and Corporate Sustainability Reporting. Journal of Business Ethics, 118, 13–29. Neergaard, P., & Pedersen, E. R. (2017). Corporate Social Behaviour: Between the Rules of the Game and the Law of the Jungle. In Business, Capitalism and Corporate Citizenship (pp. 54–72). Routledge. Nikolaeva, R., & Bicho, M. (2011). The role of institutional and reputational factors in the voluntary adoption of corporate social responsibility reporting standards. Journal of the Academy of Marketing Science, 39, 136–157. North, D. C. (1990). Institutions, institutional change and economic performance. Cambridge university press. Powell, W. W., & DiMaggio, P. J. (2012). The New Institutionalism in Organizational Analysis. University of Chicago press. Püchel, L., Wang, C., Buhmann, K., Brandt, T., von Felizia, S., Edinger-Schons, L. M., vom Brocke, J., Legner, C., Teracino, E., & Mardahl, T. D. (2024). Data and Sustainability – Leveraging Research to Bridge Data and Sustainability Transformation in Practice. Business & Information Systems Engineering, 6. Rennings, K., & Rammer, C. (2011). The impact of regulation-driven environmental innovation on innovation success and firm performance. Industry and Innovation, 18(3), 255–283. Rivera, J. (2004). Institutional pressures and voluntary environmental behavior in developing countries: Evidence from the Costa Rican hotel industry. Society and Natural Resources, 17(9), 779–797. Scott, W. R. (2013). Institutions and Organizations: Ideas, Interests, and Identities (4th ed.). SAGE Publications. Scott, W. Richard. (2008). Institutions and organizations: Ideas and interests. Sage Publications, Inc. Suchman, MC. (1995). Managing Legitimacy: Strategic and Institutional Approaches. Academy of Management Review, 20(3), 571–610. Tate, W. L., & Ellram, L. M. (2010). Corporate social responsibility reports: A thematic analysis related to supply chain management. Journal of Supply Chain Management, 46(1), 19–44. Teracino, E. A. (2017). Value Co-creation in the Cloud: Understanding Software-as-a-Service-Driven Convergence of the Enterprise Systems and Financial Services Industries. University of Groningen. United Nations. (2022). The 17 Goals | Sustainable Development. https://sdgs.un.org/goals Van de Ven, A. H., & Poole, M. S. (2005). Alternative Approaches for Studying Organizational Change. Organization Studies, 26(9), 1377–1404. Walls, J. L., Phan, P. H., & Berrone, P. (2011). Measuring Environmental Strategy: Construct Development, Reliability, and Validity. Business & Society, 50(1), 71–115. Watson, R., Elliot, S., Corbett, J., Farkas, D., Feizabadi, A., Gupta, A., Iyer, L., Sen, S., Sharda, R., Shin, N., Thapa, D., & Webster, J. (2021). How the AIS can Improve its Contributions to the UN’s Sustainability Development Goals: Towards A Framework for Scaling Collaborations and Evaluating Impact. Communications of the Association for Information Systems, 48(1). Wixom, B. H., Sebastian, I. M., & Gregory, R. W. (2020). Data Sharing 2.0: New Data Sharing, New Value Creation. MIT CISR. Yang, C.-S. (2018). An Analysis of Institutional Pressures, Green Supply Chain Management, and Green Performance in the Container Shipping Context. Transportation Research Part D: Transport and Environment, 61, 246–260. Yin, R. K. (2009). Case Study Research: Design and Methods (4th ed.). SAGE Publications. Zampou, E., Mourtos, I., Pramatari, K., & Seidel, S. (2022). A Design Theory for Energy and Carbon Management Systems in the Supply Chain. Journal of the Association for Information Systems, 23(1), 329–372.