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assessed, such that we could identify value-related aspects by using open coding that relied on the informants' discourse. Here, one researcher performed the analysis, and another checked the coding to verify the reliability of the assessment. Similar aspects that emerged were combined to constitute the set of concepts, or first-order codes. In the second phase, the researchers started grouping those codes by theme in accordance with their similarities by iterating between the first-order codes and the raw interview transcripts; this allowed us to identify themes that were firmly grounded in the data. Via several iterations, a set of themes developed. Finally, the latter, our second- order constructs, were organized into more abstract aggregate dimen- sions useful for explaining the phenomenon. For fuller understanding of the perceived value of data for environmental sustainability, we examined the relationships among the elements identified and con- sulted the literature to support further reflection on the findings (Gioia et al., 2013). We could then employ this structure for describing the findings in detail and strengthen the resulting description with fur- ther quotes from the informants.
issues: (1) which drivers were shaping the need for and use of data at the companies; (2) what kind of specific data, for what specific need, was perceived to be the most valuable; and (3) what potential busi- ness value said data represented. In addition to the lead researcher, one or two representatives of Metsä Tissue were present for all interviews: the customer's key- account manager and a sustainability specialist. They were there not to participate actively but to hear the customer's needs and thoughts. Before all sessions, we made the background and rationale for the interview explicit in relation to serving both research interests and the company's efforts to improve in the relevant area, and we assured the participants of confidentiality, to give room for open dialog also addressing sensitive issues and possibly controversial topics. We made sure to address any questions or concerns the informants might have had about the procedure or the use of the results. Our dataset comprises one interview per company, with the main decision-maker who dealt with the demands imposed on suppliers for environmental sustainability. For one of the interviews, two representatives of the customer firm were present, and the rest had one interviewee.
4
FINDINGS
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3.4
Data analysis
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Our findings, presented below in line with the structure of the Gioia analysis depicted in Figure 2, shed light on how the study's informants perceived the value of data for environmental sustainability. The views of the informants reveal that data's perceived value for environ- mental performance has two fundamental elements: First, the inter- viewees called attention to specific traits of the data and related data management required: availability of reliable detail-level product- specific data and increased transparency of product value chains, coupled with the establishment of viable systems for data manage- ment and integration solutions that support managing and sharing the related data. Second, they identified various uses for data that should encourage environment-associated improvements. Among these uses are the creation of added value for the customers' own customers (i.e., the second tier) and for consumers, contribution to better busi- ness decisions, and guaranteed compliance with regulations (also strengthening stakeholder trust). The sections below examine infor- mants' views of these various elements in detail.
Our empirical research employed inductively oriented and content- driven data structuring and analysis informed by the Gioia methodol- ogy (Gioia et al., 2013; Magnani & Gioia, 2023). An inductive method for systematically analyzing qualitative data in a manner that main- tains openness to discovering new concepts and ideas, it offers the further benefit of enabling the informants' voices to feature promi- nently in the reporting on the research (Fontana, 2020; Fontana et al., 2021; Gioia et al., 2013). This technique is suited well to explor- ing the constructs and themes associated with data's perceived value for environmental sustainability, and it supports rich results elucidat- ing the concepts identified from the data. As our research objective was to probe the interviewees' perceptions about the value of data, our empirical data corpus contains only primary data from the inter- views conducted in phase 2. Any further data sources, like publicly available reports, were not considered since the contents presented in there are unfit for this research project. Our coding and analysis yielded not a definitive list or a ranking of value attributes but a set of examples that highlight interesting per- ceptions grounded in empirical data from an evolving field wherein several key concepts are still to be defined. We used features of the Microsoft Office suite that facilitate data analysis by allowing the wording used by the informants to guide how we organized and coded the data. This tool supported the team's easy iteration over the original data, thereby helping guarantee deep insight as to the charac- teristics of the data. The first stage built on developing first-order codes (concepts) from the research data while staying relatively close to the original material. The second stage led to processing these into second-order codes (themes) and, further, into a coherent set of aggregate dimen- sions. For the first-order coding, the full body of research data was
4.1
| The data's nature and availability, alongside
management and sharing of data
4.1.1 | Detailed product-specific data and transparency of product value chains
Informants stressed the customers' need for detailed product-specific data and increasing transparency of the full product value chains and their environmental impacts. While a significant portion of the various environmental impacts is created outside the customer's activities, the customer companies are, to an increasing extent, deemed responsible for the value chains in their entirety. Such data would equip them to
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