Unobtrusive Measures Research Articles

Behave Yourself! Behavioral Indicators of Trust in Human-Agent Teams

As advancements in artificial intelligence accelerate, there is a rise in the complexity and number of autonomous agents placed in human-agent teams (HATs). With this expansion, it is important to understand how trust in agent teammates evolves and is influenced by contextual events. In support of this, significant research has focused on the factors that influence human trust in an agent and elements that negatively impact this trust. In this research, human trust in agent teammates is typically measured via self-report surveys. Although a reliable format, surveys are not without limitations, as they can be disruptive and lack the temporal resolution needed to capture nuanced and dynamic changes in trust. As a result, researchers are adopting alternative measurement approaches that aim to unobtrusively capture measures that correlate with trust using behavioral and physiological sensors that capture measures such as voice pitch and eye tracking. However, most of these studies utilize only one unobtrusive measure, which can be indicative of various states (e.g., heart rate varies with psychological and physical responses). Given the lack of selectivity in such measures, it may be necessary to utilize multiple unobtrusive measures at once, such as real-time recorded behavioral indicators, to effectively capture changes in these indicators that are due to changes in trust over time. This paper investigated the following research questions: (1) do multiple unobtrusive indicators provide a more holistic picture of trust compared to a single behavioral indicator, and (2) are certain behavioral indicators more indicative of self-reported trust at different times during a mission (e.g., before/after a trust violation event)? Results indicated that following a significant event, changes in self-reported trust are associated with specific behaviors related to improving or mitigating perceived deficiencies in their agent teammates. These insights highlight the opportunity to integrate dynamic behavioral measures in trust assessment frameworks, specifically in scenarios where a significant event compromises trust.

Using language in boundary-making: nation-state building in the early years of the Turkish Republic (1927–1965)

Abstract In 1927, the nascent Turkish Republic engaged in its first endeavor as a nation-state to systematically account for all its citizens. Included on this census were a number of different attributes the Turkish government wanted to know about its citizenry, including language. In the Turkish context, examining the language categories on the census between 1927 and 1965 and how – they changed over time – provides insight into how the new republic was imagining its citizenry (and by extension, itself). The dynamic nature of the language categories not only reflected an ambiguity that the nascent nation-state had regarding what constituted salient ethnolinguistic category for governing purposes, it also reflected the realization and subsequent politicization of minoritized populations over time. After examining the history of the development of the Turkish census, the article outlines the use of running records like censuses to provide insight into how state apparatuses were creating taxonomies to realize (or visualize) its citizenry and the value of utilizing such unobtrusive measures in Durkheimian social facts such as those who are “Us” and those who are “Other”. This article then situates the census data in its geographical context and demonstrates how the delineation of historic parts of the country that were settled by non-Turkish communities were ones that were then later demarcated into smaller administrative borders, allowing for closer government and military oversight. While this article is an exploratory case study of the Turkish census as part of its state building process, I contend that looking at how language categories were used in the 1927–1965 censuses provides insight into the nature of the Turkish government’s imaginings regarding its citizenry. In particular, the use of language data as a proxy for ethnicity was concomitant with processes of internal border demarcation that was taking place to increase government oversight in particular non-Turkish contexts, resulting in a “new” form of minoritization and marginalization in the Turkish Republic.

No access? No problem! Taking stock of unobtrusive measures for executives’ deep-level characteristics

The deep-level characteristics (e.g., personality, ability, values) of leaders have previously been found to influence key outcomes for followers, organizations, and more. However, many widely used measures of these deep-level characteristics (e.g., self-reported Likert scales) cannot be implemented when studying high-ranking leaders due to a lack of direct access to those types of leaders. In light of this challenge, scholars have developed indirect, unobtrusive measures to capture these deep-level characteristics. In this review, we examine the four overarching approaches prior scholars have used to indirectly measure leaders’ deep-level characteristics—language, visual media, financials, and personal factors—and discuss the strengths and weaknesses of each approach. Based on the patterns found, we also suggest avenues for future research.

The political consequences of wartime sexual violence: Evidence from a list experiment

Sexual violence is a prevalent feature of war with severe physical, psychological, and social consequences for survivors. Yet we have a limited understanding of how survivors relate to their political environment after the conflict ends. We analyze individual-level survey data on postwar Sri Lanka to assess whether wartime sexual victimization relates to political activism. Connecting unobtrusive measures from a list experiment to individual survivors’ political action, we show that personal experience of sexual violence increases political participation. This effect is substantial in size, holds for institutionalized and non-institutionalized forms of political action, and is robust to unobserved confounding or sample selection bias. Causal mediation analyses suggest that survivors of wartime sexual violence mobilize politically through their involvement in civic networks. The findings stress the relevance of survivors’ agency and contribute to a better understanding of wartime sexual violence, the role of civil society in post-conflict politics, and of humanitarian policy.

‘No Better Fund Without Me!’ Do Narcissistic Start-up Entrepreneurs Succeed in Venture Funding?

Unlike other narcissism research, this research uses a narcissism personality inventory (NPI) to capture the effect of startup entrepreneur’s narcissism in determining resource acquisition. Using Indonesian startup founders as the sample, the results demonstrate a positive association between narcissism and funding success, indicating that highly narcissistic startup entrepreneurs have a higher probability of success in their funding acquisition. The findings of this study are different from existing findings of the literature on narcissistic entrepreneurs that use unobtrusive measures and they support the upper-echelon theory. This research practically implies that a grandiose entrepreneur might be bad for the working environment but good for resource acquisition.

Examining Chief Executive Officer’s Narcissistic and Humility Rhetoric

Chief Executive Officer (CEO) narcissism has received considerable attention across academia and practitioner-orientated outlets. While the voluminous research stream of CEO narcissism is mostly linked to a dark personality trait, research indicates that humility has the potential to counterbalance the detrimental effects of CEO narcissism. Given the important individual and firm-level outcomes of both constructs, a systematic assessment of how rhetorical elements (i.e., language reflective of a construct) in CEO speech is still missing. We exploit conceptualizations of both constructs as “polar opposites” to derive the dimensions of both constructs and to distinguish them analytically. We further argue that subtle yet observable rhetorical cues reveal important aspects of CEOs' personality inclinations. Our results, based on qualitative documents from publicly available CEO speeches, suggest that CEOs differ in using narcissistic and humility rhetoric. This holds true even if we change the coding scheme (i.e., assessing alleged narcissistic CEOs with humble rhetoric and vice versa). Although alleged humble CEOs use narcissistic rhetoric, indicating a general trend towards confidence-inducing vocabulary and CEO speech, alleged narcissistic CEOs employ more than twice as much narcissistic rhetoric than alleged humble CEOs. Alleged narcissistic CEOs use a third of humility rhetoric as alleged humble CEOs do. Therefore, we conclude that these differences are more likely to stem from CEO personality inclinations. We discuss the value of these unobtrusive measures and warning signs in theory and practice.

Estimation of Subjectively Reported Trust, Mental Workload, and Situation Awareness Using Unobtrusive Measures.

We use a set of unobtrusive measures to estimate subjectively reported trust, mental workload, and situation awareness (henceforth "TWSA"). Subjective questionnaires are commonly used to assess human cognitive states. However, they are obtrusive and usually impractical to administer during operations. Measures derived from actions operators take while working (which we call "embedded measures") have been proposed as an unobtrusive way to obtain TWSA estimates. Embedded measures have not been systematically investigated for each of TWSA, which prevents their operational utility. Fifteen participants completed twelve trials of spaceflight-relevant tasks while using a simulated autonomous system. Embedded measures of TWSA were obtained during each trial and participants completed TWSA questionnaires after each trial. Statistical models incorporating our embedded measures were fit with various formulations, interaction effects, and levels of personalization to understand their benefits and improve model accuracy. The stepwise algorithm for building statistical models usually included embedded measures, which frequently corresponded to an intuitive increase or decrease in reported TWSA. Embedded measures alone could not accurately capture an operator's cognitive state, but combining the measures with readily observable task information or information about participants' backgrounds enabled the models to achieve good descriptive fit and accurate prediction of TWSA. Statistical models leveraging embedded measures of TWSA can be used to accurately estimate responses on subjective questionnaires that measure TWSA. Our systematic approach to investigating embedded measures and fitting models allows for cognitive state estimation without disrupting tasks when administering questionnaires would be impractical.

Who's on first? People asymmetrically attend to higher-ranked (vs. lower-ranked) competitors

Rankings, hierarchies, and competitions are an integral part of peoples' personal and professional lives and knowing one's standing vis-à-vis others helps employees decide how to outdo higher-ranked colleagues and how to refrain from being outdone by lower-ranked others. But whom do people attend to when considering these rankings? In seven studies (and five supplementary studies; N = 4496) we document a robust asymmetry in attention to higher-ranked versus lower-ranked competitors. First, using unobtrusive measures, we show that people attend more to and exhibit better memory for their higher-ranked (vs. lower-ranked) peers. Second, we demonstrate that this asymmetry is reduced when attention is shifted to lower-ranked competitors, and is moderated by participants' own standing. Finally, we find that asymmetrically attending to higher-ranked others leads people to overestimate minority representation in rankings and to make suboptimal financial decisions. We discuss implications for social comparison theory, workplace rankings, and the psychology of competition.

A Deep Dive Into Ceo Personality Literature: A Narrative Review

The last two decades have witnessed several theoretical and methodological innovations in the CEO personality literature. In light of this, this paper aims to review the current literature on CEO personality. We employ a narrative approach to integrate findings across the literature on CEO personality. Theoretically, our review highlights the positive and negative aspects associated with both positive personality traits (e.g., Big 5, SEO) and negative personality traits (e.g., narcissism). We also find several new unobtrusive measures in the literature for CEO personality. We end our review with directions for future research. This study highlights the need for a non-obtrusive CSE measurement. Finally, we stress the importance of creating a model that can be used to look at multiple CEO personality models at once.

Improving the measurement of self-regulated learning using multi-channel data

In recent years, unobtrusive measures of self-regulated learning (SRL) processes based on log data recorded by digital learning environments have attracted increasing attention. However, researchers have also recognised that simple navigational log data or time spent on pages are often not fine-grained enough to study complex SRL processes. Recent advances in data-capturing technologies enabled researchers to go beyond simple navigational logs to measure SRL processes with multi-channel data. What multi-channel data can reveal about SRL processes, and to what extent can the addition of peripheral and eye-tracking data with navigational log data change and improve the measurement of SRL are key questions that require further investigation. Hence, we conducted a study and collected learning trace data generated by 25 university students in a laboratory setting, that aimed to address this problem by enhancing navigational log data with peripheral and eye-tracking data. We developed a trace-based measurement protocol of SRL, which interpreted raw trace data from multi-channel data into SRL processes. Specifically, the study compared the frequency and duration of SRL processes detected, how much duration and times of occurrences of the detected SRL processes were affected or refined. We also used a process mining technique to analyses how temporal sequencing of the detected SRL processes changed by enriching navigational log data with peripheral and eye-tracking data. The results revealed that by adding new data channels, we improved the capture of learning actions and detected SRL processes while enhancing the granularity of the measurement. In comparison to the use of navigational logs only, the completeness of temporal sequencing relationships between SRL processes with multi-channel data improved. In addition, we concluded that eye-tracking data is valuable for measuring and extracting SRL processes, and it should receive more attention in the future.

Measuring Racial Discrimination Remotely: A Contemporary Review of Unobtrusive Measures

Social-science researchers have increasingly moved from conducting their studies in a face-to-face format to an online format. Although new and innovative remote platforms afford researchers generalizability and scale, many of these platforms also tend to solicit socially desirable responses. This pattern of socially desirable responding is evident in examinations of racial discrimination, in which participants are particularly determined to present themselves as ethical and moral. In the current article, we rectify the concern between remote platforms and inauthentic participant responses by reviewing unobtrusive measures of racial discrimination. First, we conceptualize unobtrusive measures as measurements that assess a participant’s discriminatory action without the participant’s knowledge that the specific discriminatory action is under observation. Next, we review the landscape of unobtrusive studies conducted within three broad categories—audit, helping, and friendly interaction—and discuss how these measures have changed over time. Finally, we discuss how to adapt classic face-to-face measures to remote platforms and provide recommendations for implementing unobtrusive measures into remote examinations of discrimination.

Indexing dynamic collective constructs using computer-aided text analysis: Construct validity evidence and illustrations featuring team processes.

Organizational processes have been widely recognized as both multilevel and dynamic, yet traditional methods of measurements limit our ability to model and understand such phenomena. Featuring a popular model of team processes advanced by Marks et al. (2001), we illustrate a method to use individuals' communications as construct valid unobtrusive measures of collective constructs occurring over time. Thus, the purpose of this investigation is to develop computer-aided text analysis (CATA) techniques that can score members' communications into valid team process measures. We apply a deductive content validity-based method to construct CATA dictionaries for Marks et al.'s dimensions. We then demonstrate their convergent validity with subject matter experts' (SMEs) hand-coded team communications and different SMEs' behaviorally anchored rating scales based on video recordings of team interactions, using multitrait-multimethod analyses in two samples. Using a third sample of paramedics performing a high-fidelity mass casualty incident exercise, we further demonstrate the convergent validity of the CATA and SME scorings of communications. We then model the relationships among processes across episodes using all three samples. Next, we test criterion-related validity using a longitudinal dual-discontinuous change growth modeling design featuring the paramedic CATA-scored team processes as related to a dynamic performance criterion. Finally, we integrate behavioral data from wearable sensor badges to illustrate how CATA can be scored at the individual level and then leveraged to model dynamic networks of team interactions. Implications, limitations, directions for the future research, and guidelines for the application of these techniques to other collective constructs are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Bribing and Social Desirability in Peru: A Mixed Methods Approach

Objective/Context: What could lead some individuals to be more prone to lie about engaging in corruption? We contend that a psychological approach to the study of corruption can be employed to understand who lies about corrupt behavior and why. Since social desirability bias (SDB) is related to the appropriateness of behavior, conflicting social norms in a context where corruption is widespread— like in several Latin American countries—can result in SDB in the context of surveys that directly ask for past corrupt behavior. Moreover, due to conflicting norms, some subgroups of the population might be particularly affected by SDB. Methodology: Together, focus groups, list experiments, and survey data provide evidence that supports our psychological approach. Conclusion: Overall, we confirm that SDB is at work even in a context in which corruption is widespread like Peru. Statistically speaking, we only find evidence in support of the existence of gender socialization as an important source of SDB when directly reporting past bribing behavior in Peru. However, other substantive—not statistically significant—differences related to age merit further discussion and research. Originality: This work has two main contributions; first, it highlights the importance designing unobtrusive measures when studying the prevalence of corrupt practices in Latin America; and second, it shows that policy interventions to fight corruption may not be equally effective across different groups of the population.

Unobtrusive measures of prejudice: Estimating percentages of public beliefs and behaviours.

This study was concerned with how accurate people are in their knowledge of population norms and statistics concerning such things as the economic, health and religious status of a nation and how those estimates are related to their own demography (e.g age, sex), ideology (political and religious beliefs) and intelligence. Just over 600 adults were asked to make 25 population estimates for Great Britain, including religious (church/mosque attendance) and economic (income, state benefits, car/house ownership) factors as well as estimates like the number of gay people, immigrants, smokers etc. They were reasonably accurate for things like car ownership, criminal record, vegetarianism and voting but seriously overestimated numbers related to minorities such as the prevalence of gay people, muslims and people not born in the UK. Conversely there was a significant underestimation of people receiving state benefits, having a criminal record or a private health insurance. Correlations between select variables and magnitude and absolute accuracy showed religiousness and IQ most significant correlates. Religious people were less, and intelligent people more, accurate in their estimates. A factor analysis of the estimates revealed five interpretable factors. Regressions were calculated onto these factors and showed how these individual differences accounted for as much as 14% of the variance. Implications and limitations are acknowledged.

The Limited Usefulness of Social Media and Digital Trace Data for Urban Social Research

Drawing on the literature on social science methodology, this paper argues the potential research contributions from studies using social media data are limited. These data sources are conceptualized as unobtrusive measures, a longstanding category of information for social research. Although useful in some respects, digital unobtrusive measures are limited by their content poverty, focus on espoused theory, and positivist assumptions about social reality. To conclude, the paper describes the limitations of all forms of social research, and the need for mixed methods and action research.

New Measures with Big Data and Machine Learning in Strategic Leadership Research

While the field of management has long argued the importance of manager attributes in organizational outcomes, scholars are often constraint by the limitation of data availability and computational power. To overcome the challenges, scholars start to leverage the recent development of computer science in management research. This symposium is focused on new measures using big data and machine learning techniques in strategic leadership research. The goal of the symposium is to bring together active scholars to demonstrate and discuss the opportunities and challenges of applying these new unobtrusive measures. The presentations explore a wide range of manager attributes including Big Five personality, immigration status, linguistic style, and humility and their implications on organizational investment, crises management, competitive strategy, and corporate governance. Overall, the goal of this symposium is to initiate a timely conversation with the audience from STR, RM, and OB divisions and, broadly, contribute to the theme of the annual meeting “Bringing the Manager Back in Management” by offering new insights into how managers affect organizational outcomes. CEO Personality and Intangible Investments Presenter: Steven Boivie; Texas A&M U. Presenter: Joseph Harrison; Texas Christian U. Presenter: Steve C. Lim; Texas Christian U. Presenter: Lindsey Yonish; - Immigrant CEOs and Leading During a Crisis Presenter: Dany Bahar; The Brookings Institution Presenter: Prithwiraj Choudhury; Harvard U. Presenter: Britta Glennon; The Wharton School, U. of Pennsylvania To Be Remembered: Leader Linguistic Style and Entrepreneurial Firm Resource Acquisition during IPO Presenter: Wei Guo; Hong Kong Polytechnic U. CEO Humility, Corporate Governance, and Shareholder Activism Presenter: Xina Li; INSEAD Presenter: Amy Y. Ou; Department of Management &Marketing, Faculty of Business, Hong Kong Polytechnic Presenter: Guoli Chen; INSEAD

The Health Situation of Elderly People in Poland: Three Models for Analysing the Needs and Expectations of Polish Senior Citizens: NIA, Design Thinking and GROW in the Context of the Potential Use of Medical Technologies

The article discusses the possibilities for using cross-border models in the construction of telemedicine devices which use internet connections, and the possibility for placing data ‘in the clouds’. There are such models as the Identifying and Analysing Needs model by Leslie Rae (NIA), the Design Thinking model and the GROW coaching model. The research was based on the rules of the emancipatory-critical paradigm and the triangulation method. The non-reactive research (the unobtrusive measures method) was applied in the first stage, and was based on the NIA and DT models. It was conducted based on the situation of senior citizens in Poland (against the background of the Covid-19 pandemic). In the second stage, research was carried out using a survey. The target of the survey was the elderly and their caregivers. In the third stage, in-depth interviews were conducted with senior citizens about their emotions, needs and fears/concerns. They were conducted in accordance with the GROW model coaching guidelines. The results of all the studies are presented on the basis of the Design Thinking model.

Using technology to unobtrusively observe relationship development

Texting plays an increasingly important role in romantic relationships. Along with the increased use and impact of technology in and on relationships, technology provides new approaches to observe and study relationships. This study demonstrates the potential for using unobtrusive measures of texting obtained through mobile data donation for the study of communication behaviors during relationship development. In particular, we examine how texting behaviors change prior to relationship formation and during a relationship transition. Our analysis of 1+ million text messages that 41 college-age romantic couples sent to each other during their first year of dating demonstrates (a) the utility of digital trace data for studying romantic relationship development, (b) the importance of obtaining and analyzing actual texting behaviors when observing interpersonal communication that is frequent, fast, and variable, and (c) the need for more theoretical specificity in how and why different kinds of communication behaviors change as relationships develop.

Identifying Mobile Sensing Indicators of Stress-Resilience.

Resident physicians (residents) experiencing prolonged workplace stress are at risk of developing mental health symptoms. Creating novel, unobtrusive measures of resilience would provide an accessible approach to evaluate symptom susceptibility without the perceived stigma of formal mental health assessments. In this work, we created a system to find indicators of resilience using passive wearable sensors and smartphone-delivered ecological momentary assessment (EMA). This system identified indicators of resilience during a medical internship, the high stress first-year of a residency program. We then created density estimation approaches to predict these indicators before mental health changes occurred, and validated whether the predicted indicators were also associated with resilience. Our system identified resilience indicators associated with physical activity (step count), sleeping behavior, reduced heart rate, increased mood, and reduced mood variability. Density estimation models were able to replicate a subset of the associations between sleeping behavior, heart rate, and resilience. To the best of our knowledge, this work provides the first methodology to identify and predict indicators of resilience using passive sensing and EMA. Researchers studying resident mental health can apply this approach to design resilience-building interventions and prevent mental health symptom development.

The effects of seasons and weather on sleep patterns measured through longitudinal multimodal sensing

Previous studies of seasonal effects on sleep have yielded unclear results, likely due to methodological differences and limitations in data size and/or quality. We measured the sleep habits of 216 individuals across the U.S. over four seasons for slightly over a year using objective, continuous, and unobtrusive measures of sleep and local weather. In addition, we controlled for demographics and trait-like constructs previously identified to correlate with sleep behavior. We investigated seasonal and weather effects of sleep duration, bedtime, and wake time. We found several small but statistically significant effects of seasonal and weather effects on sleep patterns. We observe the strongest seasonal effects for wake time and sleep duration, especially during the spring season: wake times are earlier, and sleep duration decreases (compared to the reference season winter). Sleep duration also modestly decreases when day lengths get longer (between the winter and summer solstice). Bedtimes and wake times tend to be slightly later as outdoor temperature increases.