Slow but Likeable? Inefficient Robots as Caring Team Members

This position paper discusses the notion of efficiency as a criterion for designing and evaluating the contributions that robots might make to human work teams. Participation in teams requires the coordination and prosecution of task-centric work activity but also requires the investment of caring social behavior as a distinctive kind of positive contribution to group interaction. Team spirit, emotional support, trust and reputation are all the outcome of such investments; they reinforce the capabilities of a team for particular joint activities, and contribute to its resilience over time. The requisite social behavior for these qualities of a team might be treated as a given in design considerations for human work teams. But the picture must change for human-robot teams: socially supportive behavior can only exist if it is explicitly designed in, and the consequent “task inefficiencies” are treated as a core part of the design equation. We draw on our own research on relational effort in social communication to offer some initial considerations about how task-inefficient action might be required for robots to engage in caring interactions with human collaborators

Reconciling the Debate on People Analytics in Academia and Practice

People analytics depicts the algorithmization of human resources management characterized by the data-driven automation and support of people-related processes or tasks. On the one hand, people analytics promises productivity increases through optimizing workforce planning, hiring, or talent development. On the other hand, the extensive data collection and analysis of employees’ behaviors can be perceived as invasive, raising privacy concerns. This debate cannot only be explained by diverging norms and values, for example, practitioners realizing commercial opportunities while being criticized by academic commentaries. Instead, an alternative explanation suggests that the opposing views can be reconciled by diving into the conceptual differences regarding what analytical methods and data sources people analytics entails. Hence, this paper proposes the conceptions of operational and strategic people analytics based on a literature review of academics’ and practitioners’ literature. Four propositions about these conceptions’ privacy and performance implications are derived. Future research should empirically validate these propositions

Slow but Likeable? Inefficient Robots as Caring Team Members

This position paper discusses the notion of efficiency as a criterion for designing and evaluating the contributions that robots might make to human work teams. Participation in teams requires the coordination and prosecution of task-centric work activity but also requires the investment of caring social behavior as a distinctive kind of positive contribution to group interaction. Team spirit, emotional support, trust and reputation are all the outcome of such investments; they reinforce the capabilities of a team for particular joint activities, and contribute to its resilience over time. The requisite social behavior for these qualities of a team might be treated as a given in design considerations for human work teams. But the picture must change for human-robot teams: socially supportive behavior can only exist if it is explicitly designed in, and the consequent “task inefficiencies” are treated as a core part of the design equation. We draw on our own research on relational effort in social communication to offer some initial considerations about how task-inefficient action might be required for robots to engage in caring interactions with human collaborators

(Non-)Participation in deliberation at work: a case study of online participative decision-making

Social media are implemented by organisations to enhance productivity and knowledge sharing among employees, but they can also support group deliberation and employee voice. This paper presents a case study of an online deliberation initiative involving the discussion of a contentious internal policy within an organisation of around 550 knowledge workers. The deliberation process lasted 5 weeks and actively involved 167 employees. Different sources of information (user interaction logs, activity patterns, questionnaire responses) were analysed to investigate the impact of participation, or non‐participation, on the level of satisfaction with the deliberation, and on the understanding of the issue discussed. The findings suggest that (1) interest is a driver for participation, but it does not explain active participation, (2) participation, either active or passive, positively influences the understanding of the issue and (3) satisfaction with the outcome is not related to participation, but it may support participation in future initiatives

Novel Alert Visualization: The Development of a Visual Analytics Prototype for Mitigation of Malicious Insider Cyber Threats

Cyber insider threat is one of the most difficult risks to mitigate in organizations. However, innovative validated visualizations for cyber analysts to better decipher and react to detected anomalies has not been reported in literature or in industry. Attacks caused by malicious insiders can cause millions of dollars in losses to an organization. Though there have been advances in Intrusion Detection Systems (IDSs) over the last three decades, traditional IDSs do not specialize in anomaly identification caused by insiders. There is also a profuse amount of data being presented to cyber analysts when deciphering big data and reacting to data breach incidents using complex information systems. Information visualization is pertinent to the identification and mitigation of malicious cyber insider threats. The main goal of this study was to develop and validate, using Subject Matter Experts (SME), an executive insider threat dashboard visualization prototype. Using the developed prototype, an experimental study was conducted, which aimed to assess the perceived effectiveness in enhancing the analysts’ interface when complex data correlations are presented to mitigate malicious insiders cyber threats. Dashboard-based visualization techniques could be used to give full visibility of network progress and problems in real-time, especially within complex and stressful environments. For instance, in an Emergency Room (ER), there are four main vital signs used for urgent patient triage. Cybersecurity vital signs can give cyber analysts clear focal points during high severity issues. Pilots must expeditiously reference the Heads Up Display (HUD), which presents only key indicators to make critical decisions during unwarranted deviations or an immediate threat. Current dashboard-based visualization techniques have yet to be fully validated within the field of cybersecurity. This study developed a visualization prototype based on SME input utilizing the Delphi method. SMEs validated the perceived effectiveness of several different types of the developed visualization dashboard. Quantitative analysis of SME’s perceived effectiveness via self-reported value and satisfaction data as well as qualitative analysis of feedback provided during the experiments using the prototype developed were performed. This study identified critical cyber visualization variables and identified visualization techniques. The identifications were then used to develop QUICK.v™ a prototype to be used when mitigating potentially malicious cyber insider threats. The perceived effectiveness of QUICK.v™ was then validated. Insights from this study can aid organizations in enhancing cybersecurity dashboard visualizations by depicting only critical cybersecurity vital signs

Social media adoption among small and medium enterprises: affordance perspective

Current study advances the understanding of adoption of social media technology in small and medium sized enterprises. Widely regarded as essential in contemporary business environments, social media have substantial effects on productivity and competitiveness. However, this research questioned the technology and did not take the technology features and functionalities for granted that predictable or has a universal properties to measure any adoption decision. In this study, in line with technology-in-practice perspective, social media is performed as a general function and no specific or has some predictable outcomes. This thesis focuses primarily on Small to Medium Enterprises (SMEs) in a developing country context, with reference to Malaysia. The thesis uses technology affordance perspective and modified Technology-Organization-Environment (TOE) model to determine antecedents and outcomes of the adoption of social media. This study employed a sequential mixed-methods research approach to meet the research objectives. The data was first collected through 17 semi-structured interviews to study the situatedness of social media in business setting, followed by a survey of 337 SMEs in Malaysia to examine the hypotheses of the extended model. The survey data were analysed using structural equation modelling (SEM). Connectivity, interaction, intimacy, flexibility, collaboration, top management support, investment and customer pressure were found to have a significant influence on SMEs’ decisions to adopt social media, while the adoption itself has a positive influence on technology paradoxes and business performance. This study has important implications and value for the research community, on how the technology has affected the changing nature of work, work practices and business environment. It contributes to enhancing our understanding about the factors influencing the adoption of social media technology in SMEs

The role of internet-based technology in customer satisfaction in the banking sector: empirical evidence from Edo State, Nigeria

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the Degree of Doctor of Philosophy.Internet-Based Technology (I-BT) has become an important resource in driving the performance of all successful businesses. This thesis contains the findings of an investigation into the role of I-BT in the relationship between customer-focused engagement behaviour (CFEBEH) and customer satisfaction (CS) in the Nigerian commercial banking sector. Using a sample of 426 bank customers in Edo State, Nigeria, the thesis seeks to ascertain whether I-BT resources in the bank have an impact on customer service delivery and satisfaction thereof. Theoretically, the Expectancy Disconfirmation Theory (EDT) and Affect Theory have been used to underpin the study of CS, while Kahn’s theory of engagement is used in support of CFEBEH. The Job Demands- Resources (JD-R) model has been used as the overarching theory underpinning this research particularly in relationship with I-BT. The results based on the structural equation model (SEM) provide two findings. First, CFEBEH has a direct effect on CS at a margin of 0.40. Second, I-BT mediates the CFEBEH and CS relationship at a margin of 0.067. Therefore, the findings of this study recommend bank managers or policymakers in Nigeria to consider making I-BT resources available in their banks as this can enhance the relationship between CFBEH and CS. By making I-BT available, this can also lead to increased CS levels, as the above results suggest. This study, therefore, has three main contributions to offer. First, by conceptualising CFEBEH as a second-order factor, this study has contributed to the literature in the area of methodology. Second, this study is the only study, to the best knowledge of the author, to have investigated the role of I-BT in the relationship between CFEBEH and CS in the Nigerian banking sector. The study has therefore deepened the academic knowledge on the role of I-BT in this relationship. Secondly, this study also contributes to the current literature on the role of I-BT in enhancing CS, particularly in a developing country context. Nigeria being the context of this study provides a unique environment for this research looking at the several challenges in the banking sector amidst institutional and infrastructural weaknesses. Finally, the design and measurement of the proposed research model in this study regarding the impact of CFEBEH on CS through its various components including PCHB, ATI, and WS, have added to the academic knowledge in customer service delivery, particularly in the banking sector which can trigger further research in this research area

Computational and Causal Approaches on Social Media and Multimodal Sensing Data: Examining Wellbeing in Situated Contexts

A core aspect of our lives is often embedded in the communities we are situated in. The interconnectedness of our interactions and experiences intertwines our situated context with our wellbeing. A better understanding of wellbeing will help us devise proactive and tailored support strategies. However, existing methodologies to assess wellbeing suffer from limitations of scale and timeliness. These limitations are surmountable by social and ubiquitous technologies. Given its ubiquity and wide use, social media can be considered a “passive sensor” that can act as a complementary source of unobtrusive, real-time, and naturalistic data to infer wellbeing. This dissertation leverages social media in concert with multimodal sensing data, which facilitate analyzing dense and longitudinal behavior at scale. This work adopts machine learning, natural language, and causal inference analysis to infer wellbeing of individuals and collectives, particularly in situated communities, such as college campuses and workplaces. Before incorporating sensing modalities in practice, we need to account for confounds. One such confound that might impact behavior change is the phenomenon of “observer effect” --- that individuals may deviate from their typical or otherwise normal behavior because of the awareness of being “monitored”. I study this problem by leveraging the potential of longitudinal and historical behavioral data through social media. Focused on a multimodal sensing study, I conduct a causal study to measure observer effect in social media behavior, and explain the observations through existing theory in psychology and social science. The findings provide recommendations to correcting biases due to observer effect in social media sensing for human behavior and wellbeing. The novelties and contributions of this dissertation are four-fold. First, I use social media data that uniquely captures the behavior of situated communities. Second, I adopt theory-driven computational and causal methods to make conclusive research claims on wellbeing dynamics. Third, I address major challenges with methods to combine social media with multimodal sensing data for a comprehensive understanding of human behavior. Fourth, I draw interpretations and explanations of online-data-driven offline inferences. This dissertation situates the findings in an interdisciplinary context, including psychology and social science, and bears implications from theoretical, practical, design, methodological, and ethical perspectives catering to various stakeholders, including researchers, practitioners, and policymakers.Ph.D