This section specifies the dimensions used in the cross-case analysis of stakeholders. The relevance of the dimensions may vary between stakeholders and use cases. Based on the case studies described earlier, this section compares the stakeholders of the BYTE project. This cross-case analysis aims to identify the commonalities of stakeholders and highlight the differences (Lammerant et al. 2015). The analysis informed the activities of the BYTE project, including big data community formation and long-term stakeholder engagement.
6.1 Technology Adoption Stage
The diffusion of innovations is a theory that seeks to explain how, why, and at what rate new ideas and technology spread through cultures. The seminal work on this theory was undertaken by Everett Rogers (Rogers 1962). He describes diffusion as the process by which an innovation is communicated through specific channels over time among the members of a social system. Adoption implies accepting something created by another or foreign to one’s nature. For a technology to be adopted by many users, it needs to be successfully diffused. Rogers describes the five adopters as follows:
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Innovators are the first 2.5% of individuals to adopt an innovation. They are adventurous, comfortable with a high degree of complexity and uncertainty, and typically have access to substantial financial resources
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Early Adopters are the next 13.5% to adopt innovation. They are well integrated into their social system and have great potential for opinion leadership. Other potential adopters look to early adopters for information and advice. Thus early adopters make excellent “missionaries” for new products or processes
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Early Majority are the next 34%. They adopt innovations slightly before the average member of a social system. They are typically not opinion leaders, but they frequently interact with their peers
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Late Majority are the next 34%. They approach innovation with a sceptical air and may not adopt the innovation until they feel pressure from their peers. They may have scarce resources
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Laggards are the last 16%. They base their decisions primarily on experience and possess almost no opinion leadership. They are highly sceptical of innovations and innovators and must feel confident that an innovation will not fail before adopting it.
In terms of technology adoption, the BYTE case studies highlight some specifics of and similarities between the stakeholders. As shown in Fig. 2, the stakeholders in these case studies follow the Rogers curve, i.e. 6% innovators, 21% early adopters, 33% early majority, 23% late majority, and 17% laggards. Some sectors are more advanced in their adoption of data technologies. For instance, the stakeholders in smart cities and crisis management case studies are either early adopters or early majority. This underlines their natural dependence on data-driven decision-making and operations. Only the stakeholders in the environment case study included innovators that encompassed space agencies and technology standards organisations. The majority stakeholders in the transport, healthcare, and culture sectors fall in the late stages of technology adoption. Therefore, some stakeholder engagement activities can be tailored towards these sectors to encourage participation in the big data community and amplification of positive externalities. Late adoption might be due to higher regulatory standards or lower levels of technology readiness.
6.2 Data Value Chain
Value chains have been used as a decision support tool to model the chain of activities that an organisation performs to deliver a valuable product or service to the market. A value chain categorises the generic value-adding activities of an organisation, allowing them to be understood and optimised. A value chain is made up of a series of subsystems, each with inputs, transformation processes, and outputs. As an analytical tool, the value chain can be applied to the information systems to understand the value-creation of data technologies. The Data Value Chain models the high-level activities that comprise an information system. A typical data value chain comprises the following activities:
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Data Acquisition is the process of gathering, filtering, and cleaning data before it is put in a data warehouse or any other storage solution on which data analysis can be carried out.
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Data Analysis is concerned with making acquired raw data easy to use in decision-making as well as for domain-specific purposes.
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Data Curation is the active management of data over its life cycle to ensure that it meets the necessary data quality requirements for its effective usage.
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Data Storage is concerned with storing and managing data in a scalable way, satisfying the needs of applications that require access to the data.
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Data Usage covers the business goals that require access to data and its analysis, and the tools needed to integrate analysis in business decision-making.
Figure 3 shows the distribution of the BYTE stakeholders in the activities associated with the Data Value Chain. Among the stakeholders analysed, 56% explicitly consider the data acquisition activities, 56% perform some form of data analysis, 44% curate data, 40% are concerned with data storage solutions, and the majority of 88% actively use data for decision-making and operations. The crisis management sector has a primary focus on data usage, with minimal consideration for data acquisition and data analysis activities. The cultural sector is mainly focused on data acquisition, curation, and usage. Designing incentives that target the specific activities of the value chain can help engage with the relevant stakeholders. The sharing of best practices from stakeholders may also serve as an incentive for engagement with the big data community. Significantly, the stakeholders can share their expertise on one type of activity on the Data Value Chain with others.
6.3 Strategic Impact of IT
The strategic impact grid is an analytical tool proposed by Nolan and McFarlan that is used by managers to evaluate their firm’s current and future information system’s needs (Nolan and McFarlan 2005). The grid defines the use of information systems resources going forward, by enabling managers to:
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Identify the current need for reliable information systems by focusing on current day-to-day operations and the functionalities of the existing information systems
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Identify future needs for new information system functionalities by focusing on the strategic role that new IT capabilities play in the organisation
Based on this analysis, the grid helps managers to identify if they need to take a defensive or offensive approach in their information systems (IS) strategy. As depicted in Fig. 4, the grid classifies the approaches into four roles:
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Support Role: Information systems constitute a tool to support and enable operations. IS are not mission-critical for current business operations. New systems offer little strategic differentiation to significantly benefit the organisation.
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Factory Role: IS infrastructure is critical to the operation of the firm. Service outages can endanger the firm’s well-being and future viability. However, limited potential exists for new systems and functionalities to make a substantial contribution to the firm.
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Turnaround Role: The firm’s current IS are not mission-critical for current business operations. However, new IS functionalities will be critical for the business’s future viability and success. The firm needs to engage in a transformation of its IT.
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Strategic Role: IS are critical to the firm’s current business operations. New IS functionalities will be critical for the future viability and prosperity of the business. Such firms have a very offensive IT posture and are proactive concerning IT investments.
Figure 5 shows the distribution of the BYTE stakeholders on the Strategic Impact Grid. Among the stakeholders analysed, 18 stakeholders were identified as having a strategic role in IT. This highlights the need to balance engagement activities to encourage participation from stakeholders in the community in other roles, which may not consider big data to be critical to their decision-making and operations management.
We also analysed the IT intensity of each case study as defined in a big data report published by McKinsey Global Institute (MGI) (Manyika et al. 2011). IT intensity indicates the ease of technology adoption and utilisation for a section. The report ranked the sectors according to their IT intensity and then divided them into five quantiles (first, second, third, fourth, fifth). The more IT assets a sector has on average, the easier it is to overcome barriers to data technologies. Each case study was mapped to the sectors indicated in the MGI report. The following list provides a summary of the analysis:
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Environment: The environment case study is mapped to the “Natural Resources” sector in the MGI report, which lies in the third quantile of IT intensity. The stakeholders in the environment case study are divided into distinct groups. The first group is focused on operations support and maintaining existing infrastructure, hence remaining in the factory role. The second group employs IT for strategic decisions and implements groundbreaking technologies, hence achieving the strategic role.
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Crisis Management: The crisis management case study is mapped to the “Health & Social Care” sector in the MGI report, which lies in the fifth quantile of IT intensity. Crisis management stakeholders require more reliable IT processes due to the mission criticality of their operations.
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Smart City: The smart city case study is mapped to the “Utilities” sector in the MGI report, which lies in the second quantile of IT intensity. Stakeholders in the smart city case study indicated the need for offensive IT strategies. This is understandable due to the data-dependent nature of the businesses and services that enable the concept of the smart city.
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Culture: The smart city case study is mapped to the “Arts, Entertainment, and Recreation” sector in the MGI report, which lies in the second quantile of IT intensity. The stakeholders of the culture case study are interested in both reliable IT and innovative IT.
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Energy: The energy case study is mapped to the “Natural Resources” sector in the MGI report, which lies in the third quantile of IT intensity. For the stakeholders in the energy case study, the role of IT is primarily strategic for both business operations and competitive advantage.
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Health: The health case study is mapped to the “Healthcare and Social Assistance” sector in the MGI report, which lies in the fifth quantile of IT intensity. The stakeholders in the heath case study are more oriented towards reliable IT, which is a prerequisite of the health sector. However, there are stakeholders that are dependent on new tools for drug discovery and improved healthcare.
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Transport: The transport case study is mapped to the “Transportation and warehousing” sector in the MGI report, which lies in the first quantile of IT intensity. In the transport case study, we observe an even distribution of the role of IT on the Strategic Impact Grid. This indicates a balance between maintaining operations through big data and using big data to gain a competitive advantage.
6.4 Stakeholder Characteristics
In addition to the dimensions introduced above, the stakeholder analysis captures a few additional attributes that are used to profile stakeholders. This section details these specific attributes and how they are represented for the purpose of analysis to establish the roles and communication needs of stakeholders. These attributes are as follows:
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Knowledge: Level of information and understanding possessed by the representative about the case study. This information is obtained by asking the representative a set of questions. Knowledge attribute could be expressed as a five-scale value: Very High, High, Average, Low, and Very Low.
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Position: Attitude and perspective of the representative towards the exercise, in terms of the degree of opposition or support expressed by the stakeholder representative. This attribute can be represented using a five-scale value: Supporter, Moderate Supporter, Neutral, Moderate Opponent, and Opponent.
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Interest: Level of interest shown by the representative in the case study, represented as a five-scale value: Very High, High, Average, Low, and Very Low.
In addition to the organisation-level analysis of stakeholder dimensions, the case studies also involved interviewing stakeholder individuals (or organisation representatives). The following figures show the distribution of stakeholders in terms of their knowledge, position, and interest (Figs. 6, 7, and 8).
Most stakeholders belong to the data providers and data users categories. This underlines the focus on the usage and exploitation of big data by the case studies. In general, the case study stakeholders rated high in terms of knowledge and interest, which could be attributable to the fact that each case study had an active big data solution. It also shows that the stakeholders across different sectors are actively involved in big data with an interest in facilitating the positive impacts of big data externalities. We coded the Likert scale for knowledge (1 to 5 scale), interest (1 to 5 scale), and position (−2 to +2 scale) levels indicated by the stakeholder individuals. Figure 9 shows the average characteristics of stakeholders to cross the case studies.
6.5 Stakeholder Influence
Identification of stakeholder influence is an important step to classify stakeholders. By understanding a stakeholder’s influence, we can better understand their relationships within the case study. Influence can be understood in terms of the amount of power a stakeholder has over the system. Influence can be both formal and informal. Formal influence is primarily based on rules or rights as laid down in legislation or formal agreements (i.e. law and rights to enforce the law, or usage rights). Informal influences are based on other factors such as interest groups or non-governmental organisations that can mobilise media, use resources, or lobby to put pressure on the ecosystem.
This section provides a cross-case analysis of the power or influence of the stakeholders in the data ecosystem. This cross-case analysis was performed using a questionnaire, interviews, and workshops conducted as part of the BYTE project. We provide an analysis of stakeholders in terms of their influence on the data ecosystem and its externalities (Table 2). This analysis is performed at the group level of stakeholders. The objective of the analysis is to classify stakeholder groups and organisations according to their capability to affect or influence the data ecosystem. In general, civil society organisations and citizens have low to medium influence on data ecosystems, which is a cause for concern. This is also true for stakeholders in the cultural sector. To address this, better incentives and a better engagement approach are required for these stakeholders to meaningfully contribute to the big data community.
Table 2 Influence of different data stakeholders based on case studies