Increasing Employees’ Willingness to Share: Introducing Appeal Strategies for People Analytics

Zieglmeier, Valentin; Gierlich-Joas, Maren; Pretschner, Alexander

doi:10.1007/978-3-031-20706-8_15

Cited by 5 publications

(10 citation statements)

References 29 publications

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“…While some usages of their data might be beneficial for employees, giving access to personal data poses the risk of profiling and misusage. The inherent power asymmetry and forced technology adoption exacerbate these risks [85,100]. To give employees more oversight and control in this situation of asymmetric knowledge and power, the concept of inverse transparency [14] was introduced to the workplace.…”

Section: Inverse Transparencymentioning

confidence: 99%

Decentralized Inverse Transparency with Blockchain

Zieglmeier

Daiqui

Pretschner

2023

Distrib. Ledger Technol.

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Employee data can be used to facilitate work, but their misusage may pose risks for individuals. Inverse transparency therefore aims to track all usages of personal data, allowing individuals to monitor them to ensure accountability for potential misusage. This necessitates a trusted log to establish an agreed-upon and non-repudiable timeline of events. The unique properties of blockchain facilitate this by providing immutability and availability. For power asymmetric environments such as the workplace, permissionless blockchain is especially beneficial as no trusted third party is required. Yet, two issues remain: (1) In a decentralized environment, no arbiter can facilitate and attest to data exchanges. Simple peer-to-peer sharing of data, conversely, lacks the required non-repudiation. (2) With data governed by privacy legislation such as the GDPR, the core advantage of immutability becomes a liability. After a rightful request, an individual’s personal data need to be rectified or deleted, which is impossible in an immutable blockchain. To solve these issues, we present Kovacs , a decentralized data exchange and usage logging system for inverse transparency built on blockchain. Its new-usage protocol ensures non-repudiation, and therefore accountability, for inverse transparency. Its one-time pseudonym generation algorithm guarantees unlinkability and enables proof of ownership, which allows data subjects to exercise their legal rights regarding their personal data. With our implementation, we show the viability of our solution. The decentralized communication impacts performance and scalability, but exchange duration and storage size are still reasonable. More importantly, the provided information security meets high requirements. We conclude that Kovacs realizes decentralized inverse transparency through secure and GDPR-compliant use of permissionless blockchain.

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Section: Inverse Transparencymentioning

confidence: 99%

Decentralized Inverse Transparency with Blockchain

Zieglmeier

Daiqui

Pretschner

2023

Distrib. Ledger Technol.

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“…by giving data owners greater transparency regarding how their data is used [41] or by increasing the benefits of a disclosure, e.g. through the use of appeal strategies such as incentives [40].…”

Section: Privacy Calculus and Disclosure Decisionsmentioning

confidence: 99%

“…To increase the appeal of data disclosure, appeal strategies can be employed. Zieglmeier et al [40] define three dimensions: Values, which are instilled into the PA tool at design time, tool-independent incentives, which managers can provide to employees when the PA system is already in use, and benefits. Benefits are inherent affordances of a PA tool and "inherent to the usage of the tool and irrevocably connected to it" [40, p. 219].…”

Section: Benefits As An Appeal Strategymentioning

confidence: 99%

“…by providing additional transparency [41], or increasing the appeal of the decision. To accomplish the latter, Zieglmeier et al [40] define three types of appeal strategies: values, incentives, and benefits. Unlike values, which guide the development of PA in an abstract manner and incentives, which are external to the tool, benefits are connected to the usage of the tool and therefore distributed during its usage [40].…”

Section: Introductionmentioning

confidence: 99%

“…To accomplish the latter, Zieglmeier et al [40] define three types of appeal strategies: values, incentives, and benefits. Unlike values, which guide the development of PA in an abstract manner and incentives, which are external to the tool, benefits are connected to the usage of the tool and therefore distributed during its usage [40]. As an example, consider a PA tool that measures the performance of employees -one potential benefit that could be given to employees is automated feedback on where they could improve.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data Owner Benefit-Driven Design of People Analytics

Zander

Zieglmeier

2023

Proc. ACM Hum.-Comput. Interact.

Self Cite

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With increasingly digitalized workplaces, the potential for sophisticated analyses of employee data rises. This increases the relevance of people analytics (PA), which are tools for the behavioral analysis of employees. Despite this potential, the successful usage of PA is hindered by employee concerns. Especially in Europe, where the GDPR or equivalent laws apply, employee consent is required before data can be processed in PA. Therefore, PA can only provide relevant insights if employees are willing to share their data. One potential way of achieving this is the use of appeal strategies. In the design of PA, the core strategy that can be used is the inclusion of data owner benefits, such as automated feedback, that are given to employees in exchange for sharing their own data. In this paper, we examine benefits as an appeal strategy and develop four design principles for the inclusion of benefits in PA. Then, we describe an exemplary set of analyses and benefits, demonstrating how our principles may be put into practice. Based on this exemplary implementation, we describe and discuss the results of a user study (n = 46) among employees in the EU and UK. Our study investigates the factors that foster or hinder employees' consent to sharing their data with PA. Then, we introduce our data owner benefits and analyze whether they can positively influence this consent decision. Our introduced data owner benefits were, contrary to our expectations, not suited to motivate our participants to consent to sharing their data. We therefore analyze how participants judge the benefits. Participants generally appreciate having them, confirming the value of including data owner benefits when designing PA. Some of our introduced benefits negatively influenced participants' sharing decision, though, meaning that careful consideration of potential risks is required when conceptualizing them.

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Rethinking People Analytics With Inverse Transparency by Design

Zieglmeier¹,

Pretschner²

2023

Proc. ACM Hum.-Comput. Interact.

Self Cite

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Employees work in increasingly digital environments that enable advanced analytics. Yet, they lack oversight over the systems that process their data. That means that potential analysis errors or hidden biases are hard to uncover. Recent data protection legislation tries to tackle these issues, but it is inadequate. It does not prevent data misusage while at the same time stifling sensible use cases for data. We think the conflict between data protection and increasingly data-driven systems should be solved differently. When access to an employees' data is given, all usages should be made transparent to them, according to the concept of inverse transparency. This allows individuals to benefit from sensible data usage while addressing the potentially harmful consequences of data misusage. To accomplish this, we propose a new design approach for workforce analytics software we refer to as inverse transparency by design. To understand the developer and user perspectives on the proposal, we conduct two exploratory studies with students. First, we let small teams of developers implement analytics tools with inverse transparency by design to uncover how they judge the approach and how it materializes in their developed tools. We find that architectural changes are made without inhibiting core functionality. The developers consider our approach valuable and technically feasible. Second, we conduct a user study over three months to let participants experience the provided inverse transparency and reflect on their experience. The study models a software development workplace where most work processes are already digital. Participants perceive the transparency as beneficial and feel empowered by it. They unanimously agree that it would be an improvement for the workplace. We conclude that inverse transparency by design is a promising approach to realize accepted and responsible people analytics.

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Increasing Employees’ Willingness to Share: Introducing Appeal Strategies for People Analytics

Cited by 5 publications

References 29 publications

Decentralized Inverse Transparency with Blockchain

Decentralized Inverse Transparency with Blockchain

Data Owner Benefit-Driven Design of People Analytics

Rethinking People Analytics With Inverse Transparency by Design

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