Trust implies vulnerability, as stated by various scholars across disciplines (Baier, 1986; Bigley & Pearce, 1998; Lewis & Weigert, 1985). Some of the most cited definitions (e.g. Mayer et al. (1995) and Rousseau et al., 1998) contain the crucial idea that the essence of trust is an acceptance of vulnerability based on positive expectations. As Bigley and Pearce (1998, p. 407), reviewing earlier work, observe: ‘When the terms “trust” and “distrust” have been evoked in the social sciences, they almost always have been associated with the idea of actor vulnerability.’ Scholars in other disciplines such as philosophy (e.g. Baghramian et al., 2020), economics (e.g. James, 2002), education (e.g. Tschannen-Moran & Hoy, 1998), medicine (Barnard, 2016), and theology (Bruni, 2021) also define trust in the light of vulnerability. Finally, behavioural conceptualizations of trust imply risk-taking and thereby incurring vulnerability, as trusting might not be reciprocated or even allows the other party to do harm (Dasgupta, 1988; Luhmann, 1979). While vulnerability is recognised as a conceptual cornerstone in trust research, few authors delve into detailed explanations of how they specifically utilise and qualify the concept. To further complicate, fundamental controversies concerning vulnerability in trust research remain unresolved. Some researchers, for instance, view vulnerability as a deliberate decision influenced by factors like perceived trustworthiness (e.g. Mayer et al., 1995), while others, following Deutsch (1958), see vulnerability as an existential awareness of the inherent risks in relationships, which is essential for the subsequent development of trust. In this vein, the acknowledgment of ‘being at somebody’s mercy’ is a prerequisite for trust to emerge. Hence, whether we perceive vulnerability as an existential condition or as a deliberate state, its relationship with trust—whether it precedes or follows trust—should significantly influence the way we advocate for trust, model it, and measure it. However, this matter has received limited attention. With our fundamental criticism, we of course acknowledge the few notable exceptions. For instance, Misztal (2011) examines vulnerability as both a condition and outcome for trust proposing three types of vulnerability. Nienaber et al. (2015) distinguish between active vulnerability and passive vulnerability, and Weibel et al. (2023) explore vulnerability as a condition for trust and differentiate various types of active trusting based on the specific vulnerability involved. While these studies offer valuable insights, much of the existing trust research tends to be superficial in qualifying vulnerability, and at worst, it opens itself to fundamental critique. It begs the question: What is the value of trust research if it fails to address the core underlying issue of vulnerability with greater precision and depth? In addition to lacking more sophisticated conceptualizations, mainstream trust research has poorly addressed the empirical experience of vulnerability and how individuals succeed or fail to accept it within the context of trust. Only a few studies have specifically examined the perception and management of vulnerability and relational risk in practical settings (Searle et al., 2016; Siegrist, 2021; Tsui-Auch & Möllering, 2010). Incorporating insights from fields that are often overlooked in trust research would provide much-needed additional understanding. For example, psychodynamics offers a comprehensive exploration of vulnerability, development, and trust through rich phenomenological studies (e.g. Corlett et al., 2021). Furthermore,

AbstractIn this chapter, we discuss the implications of how smart technology is experienced in the workplace for employee trust. Focusing on the defining features of smart technology and how these influence social interaction, we explore how trends in the permeation of technology in workplaces can influence employee trust in their employers creating both threats and opportunities for trust in this relationship. Realising the benefits of technological development requires employees to trust the intentions and capability of their employers to manage smart technology in ways that protect employee interests. We highlight the features of smart technology that may hamper this trust and discuss how addressing concerns related to data privacy, situational normality, structural assurance, and employees’ participation in the process is crucial for protecting and building trust in the workplace.

Public and academic opinion remains divided regarding the benefits and pitfalls of datafication technology in organizations, particularly regarding their impact on employees. Taking a dual-process perspective on trust, we propose that datafication technology can create small, erratic surprises in the workplace that highlight employee vulnerability and increase employees' reliance on the systematic processing of trust. We argue that these surprises precipitate a phase in the employment relationship in which employees more actively weigh trust-related cues, and the employer should therefore engage in active trust management to protect and strengthen the relationship. Our paper develops a framework of symbolic and substantive strategies to guide organizations' active trust management efforts to (re-)create situational normality, root goodwill intentions, and enable a more balanced interdependence between the organization and its employees. We discuss the implications of our paper for reconciling competing narratives about the future of work and for developing an understanding of trust processes.

Organizations experiment with how smart technology can be used to manage employees since before COVID-19 and the possibilities seem almost limitless.However, the question of how this can be achieved without impairing the so-needed trust inside organizations is yet to answer.Hence, in this study, we employ a crisp-set QCA to investigate what trustenabling datafication control configurations look like.Drawing on unique survey data from Switzerland, we show that datafication control can go hand in hand with trust if organizations make efforts for employeecentricity.Further, we can reveal four distinct ways of how organizations can implement employee-centricity to mitigate possible trust-impairing signals that stem from augmented data-gathering and analysis capabilities.Our results contribute to the still heated debate on the duality of control and trust.They also help leaders to navigate through the unmanageable multitude of possible and even trust-toxic combinations.

Artificial Intelligence (AI) is transforming the way we work. The autonomous quality of AI and its ability to perform tasks that previously required human intelligence present a new set of trust challenges to organizations and employees and is reconfiguring the relationship between humans and technology. This symposium responds to a growing consensus of the need to understand employee trust in the context of AI at work. It showcases global research using diverse methodologies to advance novel empirical insights and conceptual developments on 1) the nature and determinants of employee trust and acceptance of AI systems at work, 2) how leaders and employees can manage and navigate AI technology adoption in a way that is enabling, human-centric, and supportive of trust, and 3) how AI integration is affecting trust relationships at work. Understanding Employee Trust in AI-Enabled HR Processes: A Multinational Survey Presenter: Steve Lockey; U. of Queensland Presenter: Nicole Gillespie; U. of Queensland Presenter: Caitlin Curtis; U. of Queensland Trust of Algorithmic Evaluations: On the Importance of Voice Opportunities and Humble Leadership Presenter: Jack McGuire; National U. of Singapore Presenter: David De Cremer; NUS Business School Presenter: Devesh Narayanan; National U. of Singapore Full speed ahead? Exploring the double-edged impact of smart workplace technology on employees Presenter: Simon Daniel Schafheitle; U. of Twente Presenter: Antoinette Weibel; U. of St. Gallen Presenter: Christophe Schank; U. of Vechta Maintaining Employee Trust in Adopting Artificial Intelligence to Augment Team Knowledge Work Presenter: Kirsimarja Blomqvist; LUT U. Presenter: Paula Strann; LUT U. Presenter: Dominik Siemon; LUT U. The Dynamics of Trust in AI and Interpersonal Trust in Organizations Presenter: Brian Park; Georgia State U. Presenter: C. Ashley Fulmer; Georgia State U. Presenter: David Lehman; U. of Virginia

Artificial Intelligence (AI) and machine learning (ML) algorithms are changing the work in many ways. One hitherto little-studied area is how these technologies are impacting leader-employee relationships, particularly employees’ trust relationships in their “flesh-and-blood” leaders. In this paper, we discuss how algorithms change the nature of leadership when some leadership functions become automated. As a consequence, employees will often find themselves in a “two-leader-situation” with resulting frictions, that create novel leadership focus areas. Three situations, in particular, can be trust-problematic in the eyes of followers: the triad relationship might (1) make responsibilities blur, (2) create conflicting decisions of human leaders and algorithms, and (3) make employees’ voice unheard. We argue that these situations can undermine employee perceptions of leaders' trustworthiness as followers might start to question a leaders’ ability, benevolence, and integrity if leaders do not understand these novel situations.

Stakeholder literature has only recently turned to analyzing troublesome relationships. Here we argue that this analysis can be enriched by introducing a concept that has been more prominently explored in organization studies: distrust as a distinct concept. We introduce distrust by also differentiating it from related constructs such as suspicion, negative reciprocity and conflicts. We then develop a stakeholder distrust model, whereby we differentiate the most proximate antecedents of stakeholder distrust and how such distrust manifests in distrust-induced behaviors. Following this we explore distrust dynamics, that is, how distrust between stakeholders and the focal organization emerges and develops, spreads across levels and possibly transgresses from suspicion to distrust (or from trust to distrust). Finally, we offer first insights for further research on how stakeholder distrust as a distinct concept can enrich stakeholder analysis, lead to a new perspective on stakeholder engagement practices and enable a profound discussion of stakeholder relationship dynamics.

Stakeholder literature has only recently turned to analyzing troublesome relationships. We introduce distrust by also differentiating it from related constructs such as suspicion, negative reciprocity and conflicts. We then develop a stakeholder distrust model, whereby we differentiate the most proximate antecedents of stakeholder distrust and how such distrust manifests in distrust-induced behaviors. Following this we explore distrust dynamics, that is, how distrust between stakeholders and the focal organization emerges and develops, spreads across levels and possibly transgresses from suspicion to distrust (or from trust to distrust). Finally, we offer first insights for further research on how stakeholder distrust as a distinct concept can enrich stakeholder analysis, lead to a new perspective on stakeholder engagement practices and enable a profound discussion of stakeholder relationship dynamics.

The goal of this article is to develop an empirically grounded framework to analyze how new technologies, particularly those used in the realm of datafication, alter or expand traditional organizational control configurations. Datafication technologies for employee-related data-gathering, analysis, interpretation, and learning are increasingly applied in the workplace. Yet there remains a lack of detailed insight regarding the effects of these technologies on traditional control. To convey a better understanding of such datafication technologies in employee management and control, we used a three-step, exploratory, multi-method morphological analysis. In step 1, we developed a framework based on 26 semi-structured interviews with technological experts. In step 2, we refined and redefined the framework in [...] and redefined the framework in four workshops with scholars specializing in topics that emerged in step 1. In step 3, we evaluated and validated the framework using potential and actual users of datafication technology controls. As a result, our refined and validated "Datafication Technology Control Configuration" (DTCC) framework comprises 11 technology control dimensions and 36 technology control elements, offering the first insights into how datafication technologies can change our understanding of traditional control configurations.

Given the prominence of corporate high trust cultures in 21st-century organizations, trust development of leaders in their followers becomes an imperative of leadership and part of a leader’s role expectations. Hence, this paper analyzes why leaders come to trust their followers and how they combine cues for trusting their followers by responding to and co-creating the specific contexts of their trust. Empirically, this study employs a concurrent nested mixed-methods design with a predominant explorative interview study with 33 medium- and top-level managers and a nested fsQCA analysis of the interview data to study effective configuration of the trust cues identified. As a result, we find four discernible trusting “recipes” of leaders towards their followers; hence four different ways how leaders shape the strategic trust imperative into “on the ground” trusting behavior. Our findings not only refine prior prominent, yet too generic models of trust but we also argue in favor of a more configurational, yet idiosyncratic view on leaders’ trusting their followers in high trust contexts.

This article analyses the determinants of citizens' trust in the European Commission. We examined four predictors of citizens' trust in political institutions: political participation, value congruence, performance outcomes and attributability of performance outcomes. We argue that these factors impact trust in the European Commission, which is a necessary precondition for making a risky investment and willingness to pay taxes, which can be understood as behavioural consequences of trust. To examine our hypotheses we have implemented a vignette study. Our analyses show that value congruence, the European Commission's perceived performance and attributability impact risky investments via trust, as expected. Political participation exerts a direct significant influence on risky investments.

Organizations increasingly rely on algorithm-based HR decision-making to monitor their employees. This trend is reinforced by the technology industry claiming that its decision-making tools are efficient and objective, downplaying their potential biases. In our manuscript, we identify an important challenge arising from the efficiency-driven logic of algorithm-based HR decision-making, namely that it may shift the delicate balance between employees' personal integrity and compliance more in the direction of compliance. We suggest that critical data literacy, ethical awareness, the use of participatory design methods, and private regulatory regimes within civil society can help overcome these challenges. Our paper contributes to literature on workplace monitoring, critical data studies, personal integrity, and literature at the intersection between HR management and corporate responsibility.

Beinahe taglich ringen neue Technologieangebote um die Aufmerksamkeit der Unternehmen. Dabei steht vermehrt die Vielzahl moglicher Vermessungspraktiken der Mitarbeitenden im Vordergrund. Diese Vermessungspraktiken, auch Datafizierung der Mitarbeitenden genannt, soll die Personalsteuerung vereinfachen – teilweise sogar automatisieren – und so zur Wertschopfung im Unternehmen beitragen. Dieser Beitrag legt dar, was Datafizierung in der Personalsteuerung genau bedeutet und wie fortgeschritten die Verbreitung dieser neuen Technologien tatsachlich schon ist.

Big Data promises to make workplace surveillance both more efficient and effective, which is why many technology companies have begun offering analytics software to human resources (HR) departments. This paper describes such Big Data-based HR analytics solutions in order to problematize two ethical challenges that HR departments are faced with today: First, the cultural context that technology companies produce Big Data solutions in leads to biases and problematicbusiness models that end up influencing analytics software in many problematic ways. Second, when HR analytics software is implemented within organizations, the quantitative logic of Big Data threatens to crowd out employees’ moral agency and integrity in favor of compliance and control. In order to mitigate these challenges, we suggest that HR management today requires increased ethical awareness, critical data literacy, and the willingness to have employees participate in value-sensitive design methods along every stage of the implementation process of workplace surveillance software.

Successful leadership in the light of accelerating digitalization means, that HR-leaders need to break with cherished habits of HR management, to strike out new (often unconventional) directions and to be courageous for evaluating the impact of planned HR policies by oneself, a la Facebook Inc., Yahoo and Google LLC. In order to make these HR experiments a real benefit for the organizations and to ensure that the top-management ‘buys in’, HR Leaders need to actively put these values into practice , i.e. walk the talk for a ‘real’ cultural transformation and enact evidence-based HR management strategies. This article presents the SUMIT Mindset for leaders, an easy-to-implement guideline with tricks and activities, leaders should follow in order to make the cultural transformation in the digital age a sustainable success to the good of both, employees and business excellence.