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Attitude Towards Humanoid Robots and the Uncanny Valley Hypothesis

Abstract

The main aim of the presented study was to check whether the well-established measures concerning the attitude towards humanoid robots are good predictors for the uncanny valley effect. We present a study in which 12 computer rendered humanoid models were presented to our subjects. Their declared comfort level was cross-referenced with the Belief in Human Nature Uniqueness (BHNU) and the Negative Attitudes toward Robots that Display Human Traits (NARHT) scales. Subsequently, there was no evidence of a statistical significance between these scales and the existence of the uncanny valley phenomenon. However, correlations between expected stress level while human-robot interaction and both BHNU, as well as NARHT scales, were found. The study covered also the evaluation of the perceived robots’ characteristic and the emotional response to them.

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The Influence of Human Resource Management Practices on Employee Work Engagement

. International Journal of Human Re-source Management, Vol. 8(3), pp.263-276. [7] Hobfoll, S.E., 1989. Conservation of Resources: A New Attempt at Conceptualizing Stress. American Psychologist, 44, pp.513-524. https://doi.org/10.1037/0003-066X.44.3.513. [8] Karatepe, O.M., 2013. High-Performance Work Practices and Hotel Employee Performance: The Mediation of Work Engagement. International Journal of Hospitality Management, Vol. 32 March, pp.132-140. [9] London, H., Higgot, K., 1997. An Employee Reward and Recognition. The TQM Maga

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Investigation Influence of Store Type on Emotional State of Consumer in the Urban Purchase

Intervening Effect of Price Fairness and Perceived Affect. Journal of Business Research , 56(7), pp.541-551. [8] Baevsky R.M., 1984. Mathematical Analysis of Changes in Heart Rate During Stress . Moscow: Nauka. [9] Baevsky, R.M., Ivanov, G.G., Chirejkin L.V., etc., 2001. Analysis of Heart Rate Variability with use Various Electrocar Diographical Systems (Methodical Recommendations). The Bulletin of Arrhythmology , 24, pp.65-85. [10] Bakker, J., Pechenizkiy, M., Sidorova, N., 2011. What's Your Current Stress Level? Detection of Stress Patterns from GSR

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Energy Cost of Running Under Hypogravity in Well-Trained Runners and Triathletes: A Biomechanical Perspective

athletes. Sports Med, 37 (7), 575-586. doi:10.2165/00007256-200737070-00002 Borg, G. (1970). Perceived exertion as an indicator of somatic stress. Scand J Rehabil Med, 2 (2), 92-98. Cohen, J. (1973). Eta-Squared and Partial Eta-Squared in Fixed Factor Anova Designs. Educational and Psychological Measurement, 33 (1), 107-112. doi:10.1177/001316447303300111 di Prampero, P. E. (1981). Energetics of muscular exercise. Rev Physiol Biochem Pharmacol, 89 , 143-222. Donelan, J. M., & Kram, R. (2000). Exploring dynamic similarity in human running using

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Leadership Style, Organizational Performance, and Change Through the Lens of Emotional Intelligence

Intelligence on Employee Satisfaction: An Empirical Study of Banking Sector. Journal of Strategic Human Resource Management, 5(1). [23] Malik, S.Z., Naeem, R., 2016. Organizational Virtuousness, Perceived Organizational Support and Organizational Citizenship Behavior: A Mediation Framework. Journal of Behavioural Sciences, 26(1), pp.113-129. [24] Malik, W.U., Javed, M., Hassan, S.T., 2017. Influence of transformational leadership components on job satisfaction and organizational commitment. Pakistan Journal of Commerce and Social Sciences, 11

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The Effects of B2C Interaction on Customer Loyalty

trust in sellers. Wu and Hsing (2006) found that consumers’ perceived interaction shapes their attitudes and purchase behavior. Levy et al. (2011) proved that the interaction with consumers promoted by enterprise could help to increase the satisfaction and enjoyment of consumers, thereby improving its relationship marketing performance. Koufaris et al. (2001) believe that the degree of online interaction positively affects the consumers’ shopping pleasure and concentration of attention. This enjoyable experience helps to maintain a good relationship with

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Understanding Big Data for Industrial Innovation and Design: The Missing Information Systems Perspective

regional and national policies aiming at promoting big data are to succeed. Such questions would bring clarity to a discussion, where there has been much confusion, misunderstanding, and opportunistic use of the term “big data” as a buzz word rather than a scientific one. Lazer et al. (2014) proposed in a very highly cited Science article in 2014 that there is a “big data hubris.” This refers to the implicit assumption that big data is a substitute for, rather than a supplement to, traditional data collection and analysis. They stress that foundational issues of

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The Flemish Performance-based Research Funding System: A Unique Variant of the Norwegian Model

of the STEM journal landscape than with the publishing landscape of the SSH ( Bonaccorsi, 2018 ). The perceived unintended consequences of the PRFS have been stressed in particular in relation to violations of research integrity: if too much pressure is put on researchers, they might lose their integrity. Although this is a general and international concern, some have pinpointed blame in particular on the Flemish PRFS ( Cornelis, 2013 ). To the best of our knowledge, no evidence for this assertion has been produced, and cases of infringement of research integrity

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Providing Research Data Management (RDM) Services in Libraries: Preparedness, Roles, Challenges, and Training for RDM Practice

“university/college,” “government agency,” “non-academic health sector,” and “others.” For this particular variable, we were interested in comparing participants from academic settings with those who were outside academia. The remaining two independent variables were levels of preparedness in providing RDM services and the degree of development that participants perceived that their institutional RDM services belonged. These two variables are related to RDM maturity ( Cox et al., 2017 ), which are good indicators of the differences in RDM services. The dependent variables

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Decision Support System for Mitigating Athletic Injuries

, Diers, D.M., & Flor, H. (2015). Recovery-stress balance and injury risk in professional football players: A prospective study. Journal of Sports Sciences, 33, 2140-2148. Lucas, P., van der Gaag, L., & Abu-Hanna, A. (2004). Bayesian networks in biomedicine and healthcare. Artificial Intelligence in Medicine, 30, 201-214. Neapolitan, R. (2004). Learning bayesian networks . Englewood Cliffs NJ: Prentice Hall. Nicholson, J., Holmes, E., Lindon, J., & Wilson, I. (2004). The challenge of modelling mammalian biocomplexity. Nature Biotechnology, 22

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