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Journal articles on the topic 'Human behavior'

Author: Grafiati

Published: 4 June 2021

Last updated: 21 September 2024

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1

Tripati, Prof Rk, and Prof Deepak Sharma. "Aggressive Behavior At Home And Human Rights." American Journal of Social Science and Education Innovations 01, no. 03 (October 21, 2019): 11–15. http://dx.doi.org/10.37547/tajssei/volume01issue03-01.

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2

Nielsen, Fran. "Human behavior." StandardView 4, no. 1 (March 1996): 36–41. http://dx.doi.org/10.1145/230871.230878.

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3

Johnson, Thomas M. "Human Behavior." Journal of Nervous and Mental Disease 183, no. 6 (June 1995): 418. http://dx.doi.org/10.1097/00005053-199506000-00017.

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4

Gnanavel, Sundar. "Human Behavior." Indian Journal of Psychological Medicine 37, no. 4 (October 2015): 478–79. http://dx.doi.org/10.1177/0975156420150401.

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5

Heriyati, Pantri. "Analyzing Factors Affect Human Behavior During Covid-19 Pandemic." Journal of Advanced Research in Dynamical and Control Systems 12, SP8 (July 30, 2020): 73–80. http://dx.doi.org/10.5373/jardcs/v12sp8/20202503.

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6

D. Wilson, T. "Human Information Behavior." Informing Science: The International Journal of an Emerging Transdiscipline 3 (2000): 049–56. http://dx.doi.org/10.28945/576.

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7

Silverman, Hirsch Lazaar. "Human Behavior Integrated." Contemporary Psychology: A Journal of Reviews 43, no. 7 (July 1998): 501–2. http://dx.doi.org/10.1037/002579.

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8

Carr, Thomas H. "Understanding human behavior." Contemporary Psychology: A Journal of Reviews 31, no. 10 (October 1986): 818. http://dx.doi.org/10.1037/024209.

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9

Loehlin, J. C., L. Willerman, and J. M. Horn. "Human Behavior Genetics." Annual Review of Psychology 39, no. 1 (January 1988): 101–33. http://dx.doi.org/10.1146/annurev.ps.39.020188.000533.

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10

Einspieler, Christa, Peter B. Marschik, and Heinz F. R. Prechtl. "Human Motor Behavior." Zeitschrift für Psychologie / Journal of Psychology 216, no. 3 (January 2008): 147–53. http://dx.doi.org/10.1027/0044-3409.216.3.147.

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The spontaneous movements of the newborn infant have a long prenatal history. From 8 weeks postmenstrual age onward the fetus moves in distinct motor patterns. There is no period of amorphic and random movements. The patterns are easily recognizable, as all of them can be seen after birth. The human neonate demonstrates a continuum of motor patterns from prenatal to early postnatal life. Around the 3rd month a major transformation of motor and sensory patterns occurs. This makes the infant more fit to meet the requirements of the extra-uterine environment. The developmental course of spontaneous movements during the first 20 weeks postterm age shows the emergence and disappearance of various movement patterns. The so-called general movements deserve special interest as they are in their altered quality a most reliable indicator of brain (dys)function with a specific prediction of later developing cerebral palsy.

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11

Dunbar, R. I. M., Anna Marriott, and N. D. C. Duncan. "Human conversational behavior." Human Nature 8, no. 3 (September 1997): 231–46. http://dx.doi.org/10.1007/bf02912493.

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12

Lyons, Peter, John S. Wodarski, and Marvin D. Feit. "Human Behavior Theory." Journal of Human Behavior in the Social Environment 1, no. 1 (January 1998): 1–21. http://dx.doi.org/10.1300/j137v01n01_01.

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13

Pattie, Douglas Craig. "Influencing human behavior." Annals of Tourism Research 20, no. 4 (January 1993): 786–88. http://dx.doi.org/10.1016/0160-7383(93)90103-a.

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14

Browne, Glenn J., Christy M. K. Cheung, Armin Heinzl, and René Riedl. "Human Information Behavior." Business & Information Systems Engineering 59, no. 1 (December 20, 2016): 1–2. http://dx.doi.org/10.1007/s12599-016-0458-9.

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15

Yan, Zheng, Rui Gaspar, and Tingshao Zhu. "Emerging technologies, human behavior, and human behavior and emerging technologies." Human Behavior and Emerging Technologies 1, no. 1 (January 2019): 4–6. http://dx.doi.org/10.1002/hbe2.111.

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16

Leigh, James. "Implications of Universal and Parochial Behavior for Intercultural Communication." Journal of Intercultural Communication 2, no. 2 (September 10, 2000): 1–15. http://dx.doi.org/10.36923/jicc.v2i2.377.

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Human behavior is striking for it is the same and yet different. Many common behaviors are displayed by humans around the world whatever their race or ethnic heritage. And yet there are also unique parochial behaviors among various peoples. That is behaviors that are bound to a particular culture or ethnic group in their locale or milieu. Of course the interaction of different cultures may lead to common cultural phenomena and behaviour across various human groups - hence for example, TV, blue jeans and hello are just about ubiquitous.Human behavior is largely rooted in one or both of the following: Biological heritage Cultural heritage Universal behavior, that is shared by humans around the world therefore, is based in human biological inheritance passed on through the generations of all mankind. Alternatively, behavior that is different among the various groups of mankind, is developed in our learned behavior from our social and physical surroundings.It should be no surprise therefore, to see that the interaction of diverse parochial behaviors across cultural borders often lead to unintended misunderstanding - even conflict. This misunderstanding may appear as the received meaning of the various exotic behaviors' messages are found offensive, even if they were not meant to be so.In this paper, first of all human behavior that is universal will be looked at. Then a comparative-culture view of learned parochial behavior will be taken of various unique and culturally bound behaviors. However, the overall thrust of the paper is practical. From the insights gained of human behavior, some empathy-based practical communication techniques for effective intercultural communication are listed. With their application, intercultural offence may be minimised, and co-operation and understanding maximised.

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17

Zhu, Wen Qiu, Qian Qian Li, Jun Feng Man, and Xiang Bing Wen. "Research on Human Behavior Recognition in Video Surveillance." Advanced Materials Research 255-260 (May 2011): 2276–80. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.2276.

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For effectively solving human behavior recognition in video surveillance, a novel behavior recognition model is presented. New behaviors may be produced in the process of human motion, hierarchical Dirichlet process is used to cluster monitored feature data of human body to decide whether unknown behaviors occur or not. The infinite hidden Markov model is used to learn unknown behavior patterns with supervised method, and then update the knowledge base. When knowledge base reaches a certain scale, the system can analyze human behaviors with unsupervised method. The Viterbi decoding algorithm of HMM is adopted to analyze current behavior of the human motion. The simulation experiments show that this method has unique advantage over others.

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18

Gebretsadik Estifo, Zelalem, Luo Fan, and Naveed Ahmad Faraz. "Effect of Employee Oriented Human Resource Management Practices on Counterproductive Work Behaviors." INTERNATIONAL JOURNAL OF INNOVATION AND ECONOMIC DEVELOPMENT 5, no. 2 (2019): 23–41. http://dx.doi.org/10.18775/ijied.1849-7551-7020.2015.52.2002.

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This research investigated the link between employee-orientated human resource management (EOHRM) counterproductive work behaviors targeted at individual members and organization in Ethiopia. Relaying on social exchange theory, organizational support theory, signaling theory, and relevant literature we examined how employee-oriented HRM practice affects counterproductive work behaviors by using perceived organizational support as a mediator. Data had been obtained from 555 workers and 150 supervisors from eight companies in Ethiopia. Partial least squares structural equation modeling (PLS-SEM) methods via SmartPLS has been used to determine the hypothesized links pertaining to employee-oriented HRM and counterproductive work behavior directed towards individual members and the organization. Final results confirmed that perceived organizational support fully mediated the relationship between employee-oriented HRM and counterproductive work behaviors. The impact of employee-oriented HRM on counterproductive work behavior-organizational via perceived organizational support were stronger than the impact of employee-oriented HRM on counterproductive work behavior – individual (interpersonal). The findings suggest that organizations may minimize (reduce) employees’ counterproductive work behavior by putting into action employee-oriented HRM practices that would uplift perceived organizational support that finally prevents the tendency to act against the organization and its members.

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19

Adibhatla, Sridhar. "Buckling Behavior of Human Femur with Different Hyper Elastic Materials." Journal of Advanced Research in Dynamical and Control Systems 12, no. 3 (March 20, 2020): 554–59. http://dx.doi.org/10.5373/jardcs/v12i3/20201223.

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20

Pan, Jun Shan, and Han Ping Hu. "Different Human Behavior Patterns in Web Surfing." Applied Mechanics and Materials 543-547 (March 2014): 3252–55. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.3252.

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Recently, understanding and modeling human behavior have attracted increasing interests. In this paper, we provide some statistical research on human behavior in Web surfing during special stage called Flash Crowd (FC). Approximate entropy is used to find out the FC stage. We quantify behavior patterns by interval time between two consecutive request behaviors. Kolmogorov-Smirnov statistical test is used to estimate the appropriate probability distribution function of interval time. The results show that the request behavior patterns varied in different stages.

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21

Lin, Wang, and Huang. "Construction of Human Behavior Cognitive Map for Robots." Applied Sciences 9, no. 23 (November 21, 2019): 5026. http://dx.doi.org/10.3390/app9235026.

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With the advancement of robotics, the importance of service robots in society is increasing. It is crucial for service robots to understand their environment so that they can offer suitable responses to humans. To realize the use of space, robots primarily use an environment model. This paper is focused on the development of an environment model based on human behaviors. In this model, a new neural network structure called dynamic highway networks is applied to recognize humans’ behaviors. In addition, a two-dimensional pose estimator, Laban movement analysis, and the fuzzy integral are employed. With these methods, two new behavior-recognition algorithms are developed, and a method to record the relationship between behavior and environment is proposed. Based on the proposed environmental model, robots can identify abnormal behavior, provide an appropriate response and guide a person toward the desired normal behavior by identifying abnormal behavior. Simulations and experiments justify the proposed method with satisfactory results.

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22

Vázquez, José. "Biology & Human Behavior." American Biology Teacher 65, no. 6 (August 1, 2003): 468. http://dx.doi.org/10.2307/4451537.

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23

Wood. "Human Behavior Writ Large." Evolutionary Studies in Imaginative Culture 4, no. 1 (2020): 105. http://dx.doi.org/10.26613/esic.4.1.174.

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24

Elson, Malte, Andrew K. Przybylski, and Nicole Krämer. "“Technology and Human Behavior”." Journal of Media Psychology 27, no. 4 (October 2015): 203–4. http://dx.doi.org/10.1027/1864-1105/a000170.

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25

Simon, Herbert A. "Invariants of Human Behavior." Annual Review of Psychology 41, no. 1 (January 1990): 1–20. http://dx.doi.org/10.1146/annurev.ps.41.020190.000245.

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26

Rose, Richard J. "Genes and Human Behavior." Annual Review of Psychology 46, no. 1 (January 1995): 625–54. http://dx.doi.org/10.1146/annurev.ps.46.020195.003205.

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27

Blanch, Angel, Roberto García, Jordi Planes, Rosa Gil, Ferran Balada, Eduardo Blanco, and Anton Aluja. "Ontologies About Human Behavior." European Psychologist 22, no. 3 (July 2017): 180–97. http://dx.doi.org/10.1027/1016-9040/a000295.

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Abstract. The development of information and communication technologies has stimulated a variety of data and informational resources about human behavior. This is contributing toward collaborative efforts in the formalization and systematization of an overwhelming volume of scientific information. Several tools are helpful for this endeavor, among which the ontology is growing in popularity. Most of the available informational resources adopt the ontology to organize a shared conceptualization of a given body of knowledge. In the present study, we reviewed ontology resources (n = 17) that can be of interest to researchers and scholars involved in human behavior and psychological research. The selected ontologies were contrasted on the three main components of ontologies, classes, individuals, and properties, and on scheme and knowledge metrics. Moreover, we recorded the associations of the terms within a given ontology with terms of other ontologies (mappings), the number of projects using a particular ontology, and whether an ontology was available within the Bioportal, an extensive repository about biomedical ontologies. A few working examples were also provided to clarify how these resources might contribute to improve the analysis, understanding, and research cooperation about human behavior and psychological research.

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28

Johnson, Kim K. P., Jeong-Ju Yoo, Minjeong Kim, and Sharron J. Lennon. "Dress and Human Behavior." Clothing and Textiles Research Journal 26, no. 1 (January 2008): 3–22. http://dx.doi.org/10.1177/0887302x07303626.

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29

Gaulin, Steven J. C. "Adaptation and human behavior." Evolution and Human Behavior 22, no. 6 (November 2001): 431–38. http://dx.doi.org/10.1016/s1090-5138(01)00080-0.

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30

Russell, Michael A. H. "Smoking and human behavior." Biological Psychology 30, no. 1 (February 1990): 91–94. http://dx.doi.org/10.1016/0301-0511(90)90093-c.

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31

GIOVACCHINI, PETER L. "Evolution and Human Behavior." American Journal of Psychiatry 159, no. 2 (February 2002): 334–35. http://dx.doi.org/10.1176/appi.ajp.159.2.334.

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32

Fischer, Joern, Robert Dyball, Ioan Fazey, Catherine Gross, Stephen Dovers, Paul R. Ehrlich, Robert J. Brulle, Carleton Christensen, and Richard J. Borden. "Human behavior and sustainability." Frontiers in Ecology and the Environment 10, no. 3 (February 27, 2012): 153–60. http://dx.doi.org/10.1890/110079.

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33

Takahashi, Hideaki. "Human process control behavior." Proceedings of the Annual Convention of the Japanese Psychological Association 75 (September 15, 2011): 3AM091. http://dx.doi.org/10.4992/pacjpa.75.0_3am091.

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34

Kelman, Herbert C. "Manipulation of Human Behavior." Prevention in Human Services 8, no. 2 (December 14, 1990): 23–41. http://dx.doi.org/10.1300/j293v08n02_03.

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35

KAWASAKI, Chisato. "Behavior of Human Neonates." Congenital Anomalies 34, no. 4 (December 1994): 329–35. http://dx.doi.org/10.1111/j.1741-4520.1994.tb00802.x.

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36

Whybrow, Peter C. "Hormones and Human Behavior." Journal of Nervous and Mental Disease 175, no. 2 (February 1987): 124–25. http://dx.doi.org/10.1097/00005053-198702000-00017.

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37

Burgos, Daniel, Baltasar Fernández-Manjón, and Griff Richards. "Computers in human behavior." Computers in Human Behavior 24, no. 6 (September 2008): 2475–76. http://dx.doi.org/10.1016/j.chb.2008.03.008.

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38

Watson, Stanley J. "Hormones and Human Behavior." Psychoneuroendocrinology 12, no. 3 (January 1987): 241. http://dx.doi.org/10.1016/0306-4530(87)90011-4.

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39

Takagi, Motohiro. "Human-behavior-understanding Engine: Video-recognition AI Library for Understanding Human Behavior." NTT Technical Review 21, no. 12 (December 2023): 37–42. http://dx.doi.org/10.53829/ntr202312ra1.

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40

Gullickson, Terri, and Brigittine French. "Review of Human Ecology as Human Behavior." Contemporary Psychology: A Journal of Reviews 40, no. 1 (January 1995): 73. http://dx.doi.org/10.1037/003368.

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41

Amin, Shofia, Amirul Mukminin, Rike Setiawati, and Fitriaty Fitriaty. "Empowering leadership and human resources through stimulating innovative behaviors in higher education." Zbornik radova Ekonomskog fakulteta u Rijeci: časopis za ekonomsku teoriju i praksu/Proceedings of Rijeka Faculty of Economics: Journal of Economics and Business 39, no. 2 (December 31, 2021): 381–99. http://dx.doi.org/10.18045/zbefri.2021.2.381.

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In the era of the Fourth Industrial Revolution, 4IR or Industry 4.0, since highly advanced technology largely replaced human works, many production activities of both goods and services were required to be innovative. The purposes of this study were to examine the role of energizing self-efficacy as a mediator of leadership empowering and innovative behavior to analyze the mediator’s role of affective commitment between the two. A survey method was used by distributing online questionnaires to 617 lecturers in Indonesia. For researching the interrelation of empowering leadership, energizing self-efficacy, affective commitment, and innovative behavior via statistical examination of their interrelationship, we applied Stata 13 software to test the hypotheses. The results pinpointed the significant impact of empowering leadership on affective commitment, energizing self-efficacy, and innovative behavior. Energizing self-efficacy significantly influenced innovative behavior, but the impact vice versa is not significant. There was an indirect effect of empowering leadership on innovative behavior through energizing self-efficacy, but the affective commitment was not a mediator between empowering leadership and innovative behavior. These findings indicated that lecturers could stimulate their innovative behaviors by increasing their self-efficacy through empowering leadership. Our research findings highlight the importance of enhancing innovative behavior, self-efficacy, and empowering leadership

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42

Sowmya, M., M. Balasubramanian, and K. Vaidehi. "Human Behavior Classification using 2D – Convolutional Neural Network, VGG16 and ResNet50." Indian Journal Of Science And Technology 16, no. 16 (April 27, 2023): 1221–29. http://dx.doi.org/10.17485/ijst/v16i16.199.

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43

Ruzmatova, Gulnoz. "ABU NASR FORABI AND ERICH FROMM'S COMPARATIVISTIC VIEWS ON HUMAN BEHAVIOR." Frontline Social Sciences and History Journal 4, no. 7 (July 1, 2024): 53–66. http://dx.doi.org/10.37547/social-fsshj-04-07-06.

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The historical experience of the East and the West, the traditions of intense philosophical debates, the traditions of philosophical debates, values are extremely important. Using Erich Fromm and Farobi's scientific activities, rich, cultural and spiritual heritage, it is important to use them to build a civil society in our country, to improve spiritual life, and first of all, to educate the young generation into a well-rounded generation. studying the cultural heritage and high spiritual values of our people deeply and comprehensively, inculcating them in the mind of every person living in our Republic, especially educating the young generation to be spiritually mature and selfless people is one of the most urgent problems today. To improve the spiritual and professional potential of the leading personnel by studying and applying the works of Erich Fromm and Farobiy, to educate them in the spirit of patriotism, hard work and patriotism. In particular, the spiritual heritage of scholars is of incomparable importance in protecting young people from vices alien to the way of life and spirituality of our people.

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44

Dogan, Onur, Antonio Martinez-Millana, Eric Rojas, Marcos Sepúlveda, Jorge Munoz-Gama, Vicente Traver, and Carlos Fernandez-Llatas. "Individual Behavior Modeling with Sensors Using Process Mining." Electronics 8, no. 7 (July 9, 2019): 766. http://dx.doi.org/10.3390/electronics8070766.

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Understanding human behavior can assist in the adoption of satisfactory health interventions and improved care. One of the main problems relies on the definition of human behaviors, as human activities depend on multiple variables and are of dynamic nature. Although smart homes have advanced in the latest years and contributed to unobtrusive human behavior tracking, artificial intelligence has not coped yet with the problem of variability and dynamism of these behaviors. Process mining is an emerging discipline capable of adapting to the nature of high-variate data and extract knowledge to define behavior patterns. In this study, we analyze data from 25 in-house residents acquired with indoor location sensors by means of process mining clustering techniques, which allows obtaining workflows of the human behavior inside the house. Data are clustered by adjusting two variables: the similarity index and the Euclidean distance between workflows. Thereafter, two main models are created: (1) a workflow view to analyze the characteristics of the discovered clusters and the information they reveal about human behavior and (2) a calendar view, in which common behaviors are rendered in the way of a calendar allowing to detect relevant patterns depending on the day of the week and the season of the year. Three representative patients who performed three different behaviors: stable, unstable, and complex behaviors according to the proposed approach are investigated. This approach provides human behavior details in the manner of a workflow model, discovering user paths, frequent transitions between rooms, and the time the user was in each room, in addition to showing the results into the calendar view increases readability and visual attraction of human behaviors, allowing to us detect patterns happening on special days.

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45

Dymond, Simon, Bryan Roche, and Dermot Barnes-Holmes. "The Continuity Strategy, Human Behavior, and Behavior Analysis." Psychological Record 53, no. 3 (July 2003): 333–47. http://dx.doi.org/10.1007/bf03395449.

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46

Song, Fanpeng, Jianliang Wu, Suohai Fan, and Fei Jing. "Transcendental behavior and disturbance behavior favor human development." Applied Mathematics and Computation 378 (August 2020): 125182. http://dx.doi.org/10.1016/j.amc.2020.125182.

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47

Ward, Todd A., and Ramona Houmanfar. "Human Simulations in Behavior Analysis (1987–2010): Facilitating Research in Complex Human Behavior." Behavior and Social Issues 20, no. 1 (May 2011): 72–101. http://dx.doi.org/10.5210/bsi.v20i0.3707.

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48

BROMAGE, TIMOTHY G. "Bioarchaeology: Interpreting Behavior fro the Human Skeleton:Bioarchaeology: Interpreting Behavior fro the Human Skeleton." American Anthropologist 106, no. 1 (March 2004): 197–98. http://dx.doi.org/10.1525/aa.2004.106.1.197.

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49

OU, YONGSHENG, HUIHUAN QIAN, XINYU WU, and YANGSHENG XU. "REAL-TIME SURVEILLANCE BASED ON HUMAN BEHAVIOR ANALYSIS." International Journal of Information Acquisition 02, no. 04 (December 2005): 353–65. http://dx.doi.org/10.1142/s0219878905000714.

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This paper introduces a real-time video surveillance system which can track people and detect human abnormal behaviors. In the blob detection part, an optical flow algorithm for crowd environment is studied experimentally and a comparison study with respect to traditional subtraction approach is carried out. The different approaches in segmentation and tracking enable the system to track persons when they change movement unpredictably in occlusion. We developed two methods for the human abnormal behavior analysis. The first one employs Principal Component Analysis for feature selection and Support Vector Machine for classification of human behaviors. The proposed feature selection method is based on the border information of four consecutive blobs. The second approach computes optical flow to obtain the velocity of each pixel for determining whether a human behavior is normal or not. Both algorithms are successfully developed in crowded environments to detect the following human abnormal behaviors: (1) Running people in a crowded environment; (2) falling down movement while most are walking or standing; (3) a person carrying an abnormal bar in a square; (4) a person waving hand in the crowd. Experimental results demonstrate these two methods are robust in detecting human abnormal behaviors.

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50

Dobrydnev, S. I., and T. S. Dobrydneva. "APPLYING CONSUMER BEHAVIOR MODEL TO THE STUFF LABOR MOTIVATION." Economics Profession Business, no. 2 (June 10, 2021): 44–49. http://dx.doi.org/10.14258/epb202121.

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The article appeals to the problem of designing motivation model for the labor behavior of company stuff. Human behavior is one of the key areas of research in many fields of knowledge. The main forms of human behavior in economics are consumer and labor behavior. For each of them, extensive theoretical and practical material has been developed, a significant variety of behaviors has been proposed. Moreover, in the absence of general models of human behavior that would be applicable in any field of his activity, each science develops its own methodological apparatus and builds models based on its own approaches. Models of consumer behavior describe a clearly defined object (purchasing act), are specific and practically oriented. Patterns of labour behaviour are more general and relate to conduct in general, but not to a specific act of activity. The article attempts to apply the principles of building models of consumer behavior to modeling labor behavior. The model of type “Definition of target actions — Stimulus selection — Information and desire — Choice and location — Check and preference — Confirmation and relation” is proposed. The content of these stages for the task of changing labor behavior is shown. A methodological feature of the model is the isolation of rational and emotional aspects in some elements of labor behavior.

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