Towards Sustainable Data Management

Towards Sustainable Data Management

Student number: 10184821

University of Amsterdam Faculty of Science

Thesis Master Information Studies: Business Information Systems Final version: 23 July 2018

Supervisor: Dick Heinhuis Examiner: Tom van Engers

Abstract. The increasing amount of data is creating concern and has the potential to be costly for organizations as well as for the environment. Given that storage is largely dependent on energy, the digital era is placing a strain on limited energy sources. The aim of this study is gain a clearer perception on how organizational behavior towards sustainable data management can be improved. Exploratory research was conducted using elements from grounded theory methodology. Informal in-depth interviews with experts revealed that technological, economic, legislation, organizational, risk and security, and environmental, were the key categories identified that influence sustainable data management. This study clarifies why sustainable data management is currently in its infancy and recommends how organizational behavior towards sustainable data management can be improved by introducing hypotheses within the identified categories on which future research should elaborate.

Keywords. Sustainable data management, green information systems, organizational behavior data, data hoarding

Introduction

Organizations are in constant change due to the increasing value that information technology (IT) brings to their enterprises (Chan & Reich, 2007). The phenomenal reach of networks in our digital data society of the 21st century has resulted in an exponential growth of data storage across both cloud and localized systems. Moore’s law (Moore, 1995) predicts a massive increase in requirements for data storage (Coffman & Odlyzko, 2002; Morley, Widdicks, & Hazas, 2018). The hoarding of data is generating concern and has the po- tential to be costly for organizations as well as for the environment (Gormley & Gormley, 2012; Sweeten, Sillence, & Neave, 2018). Given that storage is largely dependent on energy, the digital era is placing a strain on limited energy sources. Although per unit computation power and unit energy efficiency is improving, the absolute energy usage of information systems has risen dramatically and is projected to continue to rise in future scenarios (Santoyo-Castelazo & Azapagic, 2014). Industry surveys show that the volume of raw data stored in corporate data centers is doubling in

size every other year (Tallon, Ramirez, & Short, 2013). According to IBM, in 2016 2.5 quintillion bytes of data is created daily and 90 percent of the world’s data had been created in the previous two years (Chauhan, Agarwal, & Kar, 2016; Koh et al., 2015). This massive data growth creates CO2 emission and has resulted in data centres being responsi- ble for 2 percent of the global CO2 emissions. This is approximately equal to the emissions coming from the global aviation industry (Avgerinou, Bertoldi, & Castellazzi, 2017). The International Data Corporation (IDC) (2017) forecasts that by 2025 the global datasphere will grow to 163 zettabytes (ZB) which is ten times the 16.1ZB of data generated in 2016. Data is predicted to grow at a rate of 42 percent which will consequently have an enormous impact on the environment (Avgerinou et al., 2017).

Green Information Systems (Green IS) has emerged as a sub- discipline within Information Systems (IS) to address crucial environmental issues such as global climate change, energy consumption and greenhouse gas emissions (Brocke, Watson, Dwyer, Elliot, & Melville, 2012; Hiralal, 2017). It provides the IS discipline with a potential contribution to help mitigate the effects on the environment caused by the Anthropocene period (Brocke et al., 2012; Melville, 2010; Hiralal, 2017). Green IS entails the opportunity to mitigate anthropogenic effects by providing sus- tainable solutions and can help motivate behavioral change on an individual, organizational, and societal level through designing sustainable information systems (Brocke et al., 2012; Wang, Chen, & Benitez-Amado, 2015; Hiralal, 2017).

A special interest group on Green IS, namely SIGGreen, was established in 2010 as a means to foster research on Green IS. SIGGreen focuses on topics such as research theories and methods, education, solutions and behavioural change to name a few (Brocke et al., 2012; Hiralal, 2017). In addition, SIGGreen has been present at various conferences from 2010 onwards such as the International Conference on Information Systems (ICIS) and at the European Conference on Information Systems (ECIS). A panel was formed at the 2012 ICIS that focused on directives for the IS discipline such that the potential of Green IS can be realized (Brocke et al., 2012; Hiralal, 2017). Five directives were proposed at the Green IS panel at ICIS in 2012, from which directive four, the need to reach out to non-IS scholars of environmental issues for discussions, critique, insights, and possible collaboration, is the focus in this paper (Hiralal, 2017).

In order to achieve the Green IS initiatives, the growth of data storage makes it not only necessary to pay attention to the hard- and software dimensions of information systems but also to the value of the data stored. Reducing the amount of data and records in organizations helps in allowing organizations to tackle the growth of data storage and to realize the objectives of green IS. Data management can thus play a fundamental role in identifying an organization’s IS data with regard to its

environmental impact. It portrays a framework for analyzing and managing data that is both crucial and necessary for any sus- tainability transformation. To tackle the energy problem as a direct effect of data storage and to realize the objectives of Green IS it is necessary to shift the focus of data management from a pure performance optimization perspec- tive to an energy efficiency optimization perspective (Harizopoulos, Shah, Meza, & Ranganathan, 2009). Although extensive research has been carried out on Green IS as a discipline, no single study exists which applies Green IS initiatives onto data management. Applying data management from a green perspective onto data storage within organizations has the potential to provide insights and improvement trajectories. To gain this insight, the causal factors that influence and lead to sustainable data management will be investigated.

Although currently not portrayed as an urgent issue within the IS discipline, this paper takes the stance that the increase in data and its corresponding stor- age will have a major impact on the environment. Starting from a broad scope of the current state of data management regarding the increase of data storage, this paper will then narrow its scope to encompass the relationships between organizational behavior and sustainable data management. Identifying the cur- rent elements that influence sustainable data management will aid in developing a more sustainable vision by providing the first building blocks towards a road map for a sustainable data management transformation. The barriers to deleting digital data, specifically in organizational settings at an organizational-level demands further exploration. This objective of this study is to gain a more comprehensive understanding of data management solutions for Green IS in tackling the increase of data storage at organization-level. Therefore, the following main research question arises:

1. How can sustainable data management be enhanced within an organization? In order to answer the main research question, the following two sub-questions will need to be answered first:

1a. What are the key elements that influence organizational behavior towards sustainable data management?

1b. Why are organizations not actively involved in optimizing their data storage with regard to its environmental impact?

1. Methodology

This research project was exploratory and interpretive in nature. Elements from grounded theory methodology were used to identify the current state of data management and its relation with sustainable data storage. This was conducted through informal unstructured and in-depth interview analysis. An inductive approach was chosen because this research attempts to discover underlying, generative mechanisms (Bryman, 2016). Furthermore, Saunders, Lewis, and Thornhill (2009) state that “an exploratory study is a valuable means of finding out what is happening; to seek new insights; to ask questions and to assess phenomena in a new light”, which fittingly describes the intention and goal of this study. Interviewing experts in the subject was chosen as the principal way of conducting this exploratory research. To create information rich cases, purposive sampling was employed such that the chance of potential insightful findings and underlying mechanisms increased.

The issue at hand was identified and was consequently used to steer the interviews. The main topic of the interviews thus concerned the increase of data gathering at an organizational level and its corresponding increase of data storage. Although the topic was known to the interviewees, it was not meant to limit the data collection. Having the topic known aided in the creation of an organizational persona, asking the interviewees about the data management attributes and hence identifying how they behave, what they find important and most importantly why. First data management within the organization was discussed such that a basis could be formed. Upon introducing the discussion topic with regard to the increase of data, it was observed how these relate to the data

management characteristics within the organization. A total of 11 expert interviews were conducted. The transcripts of which can be found in Appendix A. Ontological considerations were made for constructivism to define the role of interviewees and their organizations as social actors impacted by external realities (Bryman, 2016). Epistemological considerations were made for principles of critical realism to conduct and conceptualize the primary research to formulate the issue at hand (Bryman, 2016). 2. Results

Although data management with regard to data storage is a complex phenomenon there have been various elements that appear to be constant through- out the interviews. There are a range of internal as well as external factors that were observed to influence organizational behavior towards the explosive growth of data within an organization. In this section the key elements that influence organizational behavior towards sustainable data management will be addressed. In addition, the barriers as well as the issues within each category that influence organizational involvement will be portrayed. The elements, barriers, and issues were derived from the interviews through coding, conceptualizing and categorization.

The intensive initial codings were compared, analyzed and grouped to form concepts. These concepts were then grouped and re-grouped until six broad categories emerged that each influence organizational behavior towards data growth (See Fig. 1). For the complete coding, conceptualization and categorization see Appendix A.

3. Analysis

3.1. Technological category

The technological category includes concepts that influence organizational behavior with regard to data storage due to technological characteristics. These include but are not limited to the rise of data growth due to the growth in industry, increase in IT devices and interfaces, digitization as well as innovation, complexity of IT

infrastructure and data pollution. A common view among interviewees was that the growth of data and its corresponding storage is in- evitable. This is a direct result from the advances in equipment and applications which consequently generate more data. In addition, the size of files and documents are also increasing due to content such as pictures and videos.

3.1.1. Technological barriers and perceived problems

Several technological barriers to implementing a more sustainable way of data

management emerged from the analysis. Data duplication is an issue that sur- faces as a direct result of complex IT infrastructure. Interviewees reported that data is often stored locally in numerous locations as central data storage is difficult due to their IT

infrastructure. Another barrier is that IT applications have different languages and this data would first require a translation in order for another IT application to comprehend it. In some cases, interviewees reported that data redundancy is a direct result from a lack of proper IT architecture.

A recurrent issue in the interviews as a consequence of the aforementioned technological concepts and poor data management in general is that the efficiency of the IT landscape decreases. Talking about this issue an interviewee said: “systems run slower when there is too much data”.

3.2. Economical category

The economical category includes concepts that influence organizational behavior with regard to data storage due to economical characteristics. Some of which are, associated costs with data management, low costs of extra data storage, employee efficiency and time it takes to clean up data, short-term and long-term financial returns corresponding to investment of cleaning up data, having an offensive data strategy in a competitive environment and profitable returns as a result of good data management.

3.2.1. Economical barriers and perceived problems

Considerable economical barriers that impact the implementation of a more sustainable way of data storage were identified from the analysis. The participants on the whole reported that it is more efficient to buy extra storage space than to have employees clean up their data. This due to the time it would potentially take as well as the low cost

of buying extra storage space. There is a trade-off between efficiency at the business level and at IT level, this as business employees create a financial yield while doing their job, not when they are busy cleaning up their data. One interviewee stated that “there are definitely ambitions to clean up data but it is unclear what this would yield financially”. This statement was contradicted by another interviewee who states that “in order to benefit from Artificial Intelligence (AI) and Business Intelligence (BI) the basis of your data has to be managed.” Continuing on the importance of good data management, another interviewee stated that: “optimization of your data, including good data storage, is important and crucial in a competitive environment.” Comparing the two results within their context it can be derived that organizations are inconclusive about the added value of data management on an economical level and trade-offs are consequently made accordingly. Investing in data management may result in a long-term economic gain. However, as long as this economical gain is not known it may not be worth the short-term investment.

A recurrent issue in the interviews as a consequence of poor data management is that there is a decrease in employee productivity. Employers may for instance not be able to locate or access their data easily which results in time loss.

3.3. Organizational category

The organizational category includes concepts that influence organizational behavior with regard to data storage due to organizational characteristics. These

include, transparency and visibility of data storage and its increase, data governance, data vision, data policy, data strategy and data management incentive. In addition, organizational motivation and culture concerning data management were identified as concepts that impact organizational behavior.

3.3.1. Organizational barriers and perceived problems

Several organizational barriers to implementing a more sustainable way of data

management emerged from the analysis. For example, one interviewee reported that the increase of data storage is not known at an organizational level which consequently makes it impossible to act on this increase. Transparency and visibility of data and data growth is thus portrayed as a barrier for organizations to pursue data management. In order to gain visibility and transparency, the entire IT landscape and data flow should be modeled. Another interviewee stated that it is not always known who is responsible for data as roles and responsibilities are not clearly given. One interviewee argued that there is still debate whether data responsibility lies with the business or the IT

department which slows down the process of data management all together. From the interviews it can be derived that the value of stored data is unclear and IT departments are unaware of the value of the data stored. Although most respondents agreed that data management is important, only a small number of respondents indicated that data storage was an issue in their organization. An interesting observation was made that at an individual level most respondents stated that data storage would have a major impact on the environment but within their organization they did not see it as their problem. One respondent stated that “data storage is not a problem within my organization, nobody has to justify their data storage so there really is no incentive to clean up our data.” Organizational culture can thus result in insufficient motivation. Several interviewees commented on the notion of an interdisciplinary approach

towards data strategy. Business professionals as well as IT experts should join forces and work together to create a data strategy.

A recurrent issue in the interviews as a consequence of the aforementioned organizational concepts is employee inefficiency. Employees can only do one thing at a time and if they are cleaning up their data they cannot be working simultaneously. 3.4. Legislation category

The legislation category includes concepts that influence organizational behavior with regard to data storage due to legislation characteristics. These include but are not limited to compliance with organizational, national and international rules and regulations as well as compliance with national and international organizational strategies.

3.4.1. Legislation barriers and perceived problems

A few legislation barriers to implementing a more sustainable way of data management surfaced from the analysis. Interviewees reported that different types of data have different laws and regulation to comply with nationally and internationally. This occasionally results in confusion within the local business departments of multinational organizations. One interviewee revealed that their organization had failed to comply with a national law which was not included in their company rules and regulations. This resulted in a lawsuit which they lost and ended up costing them a lot of money. In addition, it was noted that an organizational data policy and strategy should be in compliance with governmental policies as well as any other stakeholders.

Overall, interviewees reported that the increase in data storage has increased the importance of compliance. This because failure to do so, with its corresponding sanctions, creates an important incentive for good data management. It is still a complex issue as organizations with different types of data need different data management policies.

3.5. Risk and Security category

The risk and security category include concepts that influence organizational behavior with regard to data storage due to risk and security characteristics. Some of which are cyber security concerns, keeping data for evidence or potential value, problems of legacy data and responsible data storage.

3.5.1. Risk an Security barriers and perceived problems

Several risk and security barriers to implementing a more sustainable way of data management emerged from the analysis. Interviewees stated that clean data is a necessity for a successful digital transformation. In order to have clean data archiving, retention and responsible storage are crucial. If the aforementioned concepts are not pursued, the accessibility of the data becomes a problem which drastically increases an organizations cyber security risks. This in turn creates risks regarding privacy both for the organization as well as its stakeholders.

3.6. Environmental category

The environmental category includes concepts that influence organizational behavior with regard to data storage due to environmental impact characteristics. These include the energy footprint of data storage, involvement in sustainability, organizational environmental ambitions and greenness of data centers. The participants on the whole demonstrated an eager interest in the issue of data storage and its impacts on the environment but indicated that this was not portrayed as a problematic or urgent issue within their organizations. One participant commented that: “besides choosing a commercial cloud company that is ’green’, we are not actively involved in sustainability in this way.”

3.6.1. Environmental barriers and perceived problems

Several environmental barriers with implementing a more sustainable way of data storage emerged from the interviews. All of the participants reported that they were unaware of the energy footprint of the data storage of their organization. A majority of the participants additionally noted that they were unaware of the size of the data storage of their organization or its increase. One interviewee reported that within their organization “there are definitely environmental ambitions but it is unclear what the effects of our data storage are”, another interviewee commented that “it is unclear what optimizing data storage would yield with regard to environmental impact”. Another interviewee when asked about the environmental impact of data storage said: “I think the assumption is that data storage is infinite and because the impact is unknown there really is no reason or incentive to delete data.” These results suggest that there is an overall lack of transparency, awareness and environmental strategy with regard to data storage. 3.7. Summary of analysis

The results in this section provide evidence that the categories identified are causal to organizations behavior towards sustainable data management. The barriers and issues that emerged can therefore serve as a starting point for pursuing sustainable data management. The next section, therefore, moves on to discuss the categories in relation to previous literature in an attempt to predict how these categories can stimulate sustainable data management within an organization.

4. Discussion

The main objective of this study, to gain a clearer perception on how organizational behavior towards sustainable data management can be improved, will be addressed in this section. In order for the aforementioned categories to aid in enhancing sustainable data management they need to be understood and developed into facilitators so that incentive is created. Derived from the associations in the analysis, propositions will be made with regard to each category.

4.1. Technological proposition

The results of this study indicate that IT efficiency is a trigger for an organization to pursue data management. In accordance with the present findings, Galliers and Leidner (2014) show that congested data flows and migrating the right data in the right amount to the right location is a crucial problem for large global firms. Data governance together with technical implementation can significantly reduce an organization’s data redundancy (Alharbi, 2016; Mukherjee, 2013). These findings provide further support for the proposition that enhancing visibility of the IT landscape and data flow will enhance the pursuance of sustainable data management within an organization. Therefore, the following hypothesis arises:

1. If the visibility of the IT landscape and data flow of an organization is enhanced then this will benefit the implementation of sustainable data management.

4.2. Economical proposition

As previously mentioned, interviewees reported that economical yields, such as efficiency and finances, impact their organizations behavior towards data management. The most obvious findings to emerge from the analysis is that an organization wants to see a positive return to come from investment. Gormley and Gormley (2012) show that within organizations, costs, lifespan of data, effectiveness and productivity are areas that are highly affected by data growth. It can therefore be assumed that once data management is implemented, the economical yield will be positive leading to a higher efficiency. In addition, (Nishant, Teo, & Goh, 2013) confirm that sustainability practices can create competitive advantage for organizations. According to these findings, it can be proposed that sustainable data management will be pursued within an organization if it results in efficiency and competitive advantage. Consequently, the following hypothesis arises:

2. If data management is proven to yield efficiency then this will benefit the implementation of sustainable data management.

4.3. Legislation proposition

Interviewees portrayed legislation as having an important impact on their

organization’s data management. Compliance with all their stakeholders appeared to create an incentive towards action. This is in line with Reid and Toffel (2009) where organizations were found to be more likely to engage in practices with the aims of a social movement and conform to government regulation when threatened with sanctions. These results seem to be consistent with research by Berrone, Fosfuri, Gelabert, and Gomez-Mejia (2013) who confirm that governmental pressure influence company’s propensity to engage in environmental innovation. In order for legislation to aid in sustainable data management it is therefore proposed that stakeholders should increase their expectations and demand organizations to implement responsible data management strategies and policies. Therefore, the third hypothesis is:

3. If stakeholders require organizations to implement responsible data strategies and policies then this will benefit the implementation of sustainable data management.

4.4. Organizational proposition

Interviewees illustrated that actions towards sustainable data management within their organizations are directly aligned with their organizations involvement on the matter. One of the findings that emerged was that organizations with an offensive and clear data policy and strategy were more inclined to pursue this whereas organizations without one had no incentive to do so. There is therefore evidence that organization strategies influence organizational behavior with regard to sustainable data

management. This is supported by Branzei, Ursacki-Bryant, Vertinsky, and Zhang (2004) where greater commitment to environmental initiatives were associated with a higher level of perceived strategic commitment. Furthermore, it was found that there was better integration of environmental responsibilities among organizational members when commitment was pursued in upper echelons (Branzei et al., 2004). In order for the organization category to enhance sustainable data management is is necessary for the organization to define an organizational strategy and set clear policies with regard to their data storage. The proposition from the analysis of this study is that once a clear strategy and policy is in place, this will increase sustainable data management and stimulate initiatives (Zheng, 2014). This is supported by Loock, Staake, and Thiesse (2013) where goal setting regarding energy conservation confirmed to lead to statistically significant savings and stimulated bottom up initiatives that were aligned with the strategic views of the organization.

4. If an organizational data strategy and policy is defined and executed then this will benefit the implementation of sustainable data management.

4.5. Risk and Security proposition

The results of this study support evidence that data growth can lead to poor data quality. In additions, Vessey and Conger (1994) findings show that poor data quality can have negative social and economic impacts. These results support previous research into this area by da Silva and Danziger (2015) who confirmed that

confidentiality, integrity, and availability are three core principles within data security that guide organizations. These principles highlight the importance numerous security areas such as access control, privacy, information modification and reliable access. These findings thus provide further support for the proposition that clearly identifying data requirements will rein- force organizations to pursue sustainable data

management.

5. If data requirements are defined and executed then this will benefit the implementation of sustainable data management.

4.6. Environmental proposition

As mentioned in the analysis, there was an overall positive interest in the issue of sustainable data management. Increase in data and its corresponding storage, however, was not highlighted as a problematic or urgent matter. In addition, the results suggested that lack of awareness of data storage and its impact on the environment caused the lack of involvement of organizations. This is in line with Anderson and Bateman (2000), where framing an issue as urgent and having a local impact was found to be

fundamental to organizational action. The proposition therefore is that if the awareness and visibility of data storage impact on the environment is portrayed, sustainable data management will enhance within organizations.

6. If the impact of data storage on the environment is known then this will benefit the implementation of sustainable data management.

4.7. Further analysis

In addition to the aforementioned prior studies, evolutionary behavior theory can also help to clarify the observed organizational behavior towards sustainable data

management. When species is replaced with organizations it can both aid in an explanation for the ambition to collect and store data as well as the lack of sustainable data management within organizations. Firstly, for organizations in a competitive environment, data collection and storage can serve as an adaptive function that ensures its resilience (Lozano, 2013; Baum, 2017). This can be applied to organizations as their external environment, in this vast digital age, is continuously changing. The recurrent pattern of organizational behavior towards sustainable data management may furthermore be justifiable through evolutionary behavior theory in two ways. First, organizations cannot afford to decrease their data collection as this has the potential to weaken their position in the external environment. Second, organizations cannot afford to invest in sustainable data management due to the unawareness of the potential long- term reward. Evolutionary behavior theory suggests, in line with the findings of this study, that organizational choices are inconsistent with potential optimal strategies when making decisions over time (Kalenscher & Van Wingerden, 2011; Baum, 2017; Green, Fristoe, & Myerson, 1994; Kalenscher & Pennartz, 2008). This violates the predictions of rational economic theory which assumes orga- nizations will always maximize expected utility (Kalenscher & Van Wingerden, 2011; Vriend, 1996). The violation is based on the sole reason that the organizations are aware of the necessity of sustainable data management, however, instead choose the availability of short-term rewards, e.g. buying additional storage space, over decisions in line with their long-term interests.

5. Recommendation

Overall, this study has shown that sustainable data management within organization is currently in its infancy. It is encouraging to compare these predictions with the Diffusion of Innovation Theory of Rogers (2010) where a technological innovation is said to pass through five stages (Bose & Luo, 2011). Namely, 1. knowledge (exposure to its existence, and understanding of its functions), 2. persuasion (the forming of favorable attitude to it), 3. decision (commitment to its adoption), 4. implementation (putting it to use), and 5. confirmation (reinforcement based on positive outcomes from it) (Bose & Luo, 2011). In accordance with the results of this study and the DoI Theory, it can be concluded that sustainable data management is barely in phase one. Before organizations can pursue sustainable data management, they first need to be exposed to the existing rise of their digital data and the impact of its corresponding storage on the environment. This can be done by creating visibility and awareness which corresponds to the changes proposed by both the technological category as well

as the environmental category. Secondly, they need to form a favorable attitude. This can be done by including a data strategy and policy in their organizational strategy which is proposed in the organizational category. Thirdly, they need to commit to this strategy, this can be achieved through legislation by the organization itself or any of their stakeholders. The third and fourth stages of the DoI Theory, decision and implementation can first be achieved through the proposition made in the organizational category. Developing a clear data strategy and policy will allow organizations to commit to and implement sustainable data management. Finally,

confirmation can be achieved by the proposition made in the economical, risk and security as well as the environmental category. Positive outcomes and benefits of sustainable data management with regard to finances, risks and security, and environmental impact can reinforce organizations to pursue sustainable data management.

6. Conclusion

Analysis of the collected data provides evidence that sustainable data management is influenced by six categories. Namely technological, economical, organizational, risk and security, legislation and environmental. Therefore, it is concluded that these categories can, when acted upon, influence an organizations behavior towards sustainable data management and consequently its data storage footprint. Organizational behavior towards sustainable data management is predicted to positively increase once the barriers and issues in each category are addressed. This research demonstrates that although sustainable data management is currently in its infancy, its development can be facilitated. These findings are in agreement with Rogers (2010) where it is suggested that sustainable data management is currently in phase one of the DoI Theory. The propositions made in each category, however, have the potential to aid sustainable data management through the next four phases.

This study clarifies why sustainable data management is currently in its infancy and recommends how organizational behavior towards sustainable data management can be improved by introducing hypotheses within the identified categories on which future research should elaborate.

7. Limitations and future research

The in-depth interviews were helpful in revealing how organizations behave towards the increase in data storage and what influences their behavior. It should be recognized that conclusions are based on the interpretation of results from 11 interviews, which may not have been enough to saturate the data. The sample should in no means fully represent the overall population of organizations. The focus of this research has been on data storage in general and further research should be conducted into different types of data and different types of data storage.

Initially, the goal was to develop grounded theory regarding this issue. This was not feasible due to time constraints, instead six categories and propositions are

introduced as a basis for the development of theory. The categories nor the propositions were validated with additional data sets. This can be seen as a limitation because it

would have been more appropriate to do this. Future research should incorporate several data sets regarding the categories so that this can complement, verify or validate the observations. This would make the outcome of this research more valid and reliable regarding the concepts of confirmability, dependability and credibility (Shenton, 2004). Further research should focus on clearer conceptualization of sustainable data

management with regard to data storage to create a reliable and holistic framework of the cause and effects of the data storage increase as well as organizational behavior towards sustainable data management. In addition, it is required to explore the cause and effects of each of the established associations that were observed to influence organizational behavior. Furthermore, future research should be done regarding the propositions to further develop under- standing of sustainable data management and to substantiate further findings.

Finally, the justification through evolutionary behavioral theory, specifically the explanation of behavioral mechanisms as well as behavioral economics should be examined more closely. The outcome of such studies has the potential to add crucial theoretical findings that can be used to develop interventions aimed at improving sustainable data management within organizations. Effective quantitative methods could be used such as structured interviews and structured observation (Bryman, 2016).

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