Cyberpsychology Lecture 3.pptx

INDIVIDUAL DIFFERENCES, CONTEXT & ONLINE BEHAVIOUR Dr. George Georgiou [email protected]. uk Room: 1H272, C.P. Snow 01707 285 123 2 Does everyone use the internet / digital technology in the same way? QUESTIONS & AIMS OF THE LECTURE Are all people the same? Clearly the answer is no to both quest...

INDIVIDUAL DIFFERENCES, CONTEXT & ONLINE BEHAVIOUR Dr. George Georgiou [email protected]. uk Room: 1H272, C.P. Snow 01707 285 123 2 Does everyone use the internet / digital technology in the same way? QUESTIONS & AIMS OF THE LECTURE Are all people the same? Clearly the answer is no to both questions. Although, people with similar individual differences may exhibit similar digitally mediated behaviour 3 QUESTIONS & AIMS OF THE LECTURE Is the internet just one thing and people use it differently? Internet is not one homogenous system and it is not static Different platforms & apps, and as new ones are created or older ones evolve, new behaviours may be displayed or may emerge e.g. selfies. 4 QUESTIONS & AIMS OF THE LECTURE Can individual differences predict digitally connected /mediated behaviours What characteristics / traits could be assessed Are there differences between groups WHICH INDIVIDUAL DIFFERENCES COULD BE CONSIDERED? 6 QUESTIONS & AIMS OF THE LECTURE HOW DO CONTEXTUAL & SITUATIONAL FACTORS INFLUENCE HOW PEOPLE BEHAVE ONLINE? WHAT OTHER THEORIES OR MODELS COULD BE CONSIDERED TO EXPLAIN ‘ONLINE’ DIGITAL MEDIATED BEHAVIOURS? WHAT OTHER FACTORS NEED TO BE CONSIDERED? 8 What is ‘online behaviour’ POTENTIAL ISSUES WITH ONLINE BEHAVIOUR DEFINING CONCEPTS & MEASURES Is it the same as online data? Technology or internet ‘use’ is a broad term How to conceptualise or measure this in a meaningful way Categorical – yes/no Type of use / feature use Time 9 DEFINING CONCEPTS & MEASURES PROPOSED ONLINE BEHAVIOUR TAXONOMY: KAYE ET AL. (2022) Onlineexclusive behaviour takes place exclusively online for user and/or audience (e.g., social media interactions, emailing) Onlinemediated behaviour takes place offline for user and/or audience but are mediated by internet-enabled platforms (e.g., video calls, live-streams). Onlinerecorded behaviour offline but are concurrently or subsequently recorded online (e.g. the lecture, fitness apps. wearable devices). 10 THE USE OF DIGITAL TECHNOLOGIES ACROSS GROUPS 11 GENERATIONS It is enticing to make generalisations based on cohorts or groups Years are approximate as no standard definition or agreement on the years and span of each generation. Therefore, differences across sources. Why have labels and letters at all? Gen Alpha 2013 to 2025 Generation Z / GenZ, iGen, Post-millennials, Centennials 2000 to 2012 Generation Y / Millennials 1980 to 2000 Generation X 1965 to 1979 Baby Boomers 1946 to 1964 Silent Generation 1925 to 1945 G.I. Generation 1900 to 1924 12 DO GROUPS DIFFER MULTITASKING ACROSS GENERATIONS Carrier et al (2009) compared, Net generation, Generation X and Baby Boomers in their multitasking activities. Asked about behaviour across 12 tasks, including digitally connected ones 13 DO GROUPS DIFFER - MULTITASKING ACROSS GENERATIONS Results showed that Net Generation reported more multitasking than members of Generation X who reported more multitasking than members of the Baby Boomer generation. However, the choice of which tasks to multitask were very similar across generations and the rated difficulty of the task completed at the same time was also similar for different generations. Tasks that are frequently completed together maybe ones that do not impose as heavy ‘cognitive load’. Therefore, similar load capacities across generations (Jeong & Fishbein, 2007). Evidence of some broad generational effects, but this may have many causal factors 14 Popularisation of the terms net generation (Tapscott, 1998) and digital native (Prensky, 2001, 2009). DIGITAL NATIVES VS DIGITAL IMMIGRANTS Digital native is someone who has grown up after the widespread introduction of the personal computer and therefore been immersed in digital technology. Is also used to refer to someone who is highly adept with technology. 15 Digital immigrants are those born before the widespread adoption of computers and have had to adopt digital technology later in life. DIGITAL NATIVES VS DIGITAL IMMIGRANTS Many claimed that because of immersion in digital technologies from birth, digital natives think and learn differently from digital immigrants. Digital immigrants are argued to be less technically able than digital natives and can never develop the same level of technology skills and knowledge as digital natives. Is this true? 16 DIGITAL NATIVES VS DIGITAL IMMIGRANTS Bennett (2012), Bennett et al. (2008), & Bennett & Maton (2010) argue that there is a large body of research which has begun to challenge the idea of a uniform group of technologically adept individuals. a range in technology skills, knowledge and interests of young people, and suggests that there are important ‘digital divides’ which are ignored by the digital native concept. There will be many individual differences in interest and skill, and digital natives may have a limited range of very frequently used technology, and a low tolerance to technological challenges (want it to simply work). 17 BENNETT (2012, P.7) “…. People adopt technologies for a wide range of reasons and have diverse patterns and habits, and the skills they develop are often narrow and highly contextualised (i.e., fit for a particular purpose). As a result, it would be wrong to generalize about a section of a population on the basis of how they use technology, and in particular on the basis of presumed exposure to technology.” 18 DIGITAL NATIVES VS DIGITAL IMMIGRANTS CONCLUSIONS The term ‘digital native’ may not be useful. Digital natives cannot be considered to be one homogenous group, Should not assume that digital natives are proficient at using technology. 19 COMPARING GROUPS ON FACEBOOK CHRISTOFIDES ET AL. (2011) Assumption that adolescents share more information about themselves than adults and care less about the use of controls to protect their privacy. Participants were asked to fill in questionnaires about their information sharing and privacy controls on Facebook. ‘‘How likely are you to disclose personal information on Facebook?’’ ‘‘How likely are you to change who can see your profile (e.g., only your friends)?’’ 20 Adolesce nts More informati on Adults Used privacy settings more Adults Posted less informati on Adolesce nts Used privacy setting less RESULTS 21 RESULTS However, variables that predicted the information disclosure and privacy behaviour were the same for both groups. Time spent online mediated the behaviour in the same way for both groups. Increased time online was related to more information being shared. Overall adolescents spent more time online, but More similarities than differences between groups 22 SUMMARY - THE INTERNET AND DIFFERENT USER GROUPS Groupings such as digital natives and digital immigrants may not be meaningful. Evidence seems to suggest that there are more similarities than differences between groups in why and how they use digital technologies. Differences between generations may not be real generational effects. Time seems to be a key potential moderator of online behaviours 23 SUMMARY - THE INTERNET AND DIFFERENT USER GROUPS Maybe different age groups / generations differ in other ways to do with digital technology Haven’t learnt what it does or been taught how to use Have difficulty accessing or have limited motivation to learning how to use it Digital exclusion & digital literacy entry points The technology or platforms/ apps may be designed and marketed towards specific age groups. Social norms of their group are different Their needs are met through other means 24 INDIVIDUAL DIFFERENCES & AND THE INTERNET 25 WHAT PSYCHOLOGICAL CONSTRUCTS OR TRAITS CAN PREDICT ONLINE BEHAVIOUR Personality Broad traits e.g. Big Five Narrow traits e.g. Need to belong, Loneliness, Social anxiety / Shyness, Narcissism, Need for cognition Are these traits good predictors of online behaviour? Are there other theories or models we could consider? 26 WHAT DO WE KNOW ABOUT PERSONALITY & BEHAVIOUR Personali ty Behavio ur 27 WHAT DO WE KNOW ABOUT PERSONALITY & BEHAVIOUR Personalit y Situatio n behavio ur 28 WHAT DO WE KNOW ABOUT PERSONALITY & BEHAVIOUR personal ity situation behavio ur 29 PERSONALITY TRAITS – THE BIG FIVE (OCEAN) Openness to experience – Openness reflects the degree of intellectual curiosity, creativity and a preference for novelty and variety. Conscientiousness – A tendency to show self-discipline, act dutifully, and aim for achievement; planned rather than spontaneous behaviour Extraversion – Energy, positive emotions, assertiveness, sociability and the tendency to seek stimulation in the company of others, and talkativeness. Agreeableness – A tendency to be compassionate and cooperative rather than suspicious and antagonistic towards others. Neuroticism – The tendency to experience unpleasant emotions easily, such as anger, anxiety or vulnerability. Also refers to the degree of emotional stability and impulse control. 30 CAN PERSONALITY PREDICT WHAT KIND OF ACTIVITIES WE DO ONLINE What do you think about personality and behaviour overall? Do you think the same will apply for the internet? 31 PERSONALITY AND THE INTERNET Very early studies simply looked at the psychological implications of the TIME spent online but had not considered the different activities that users took part in (e.g., Kraut et al., 1998). 32 EARLY STUDIES OF PERSONALITY AND THE INTERNET Shortly after this studies started to consider frequency of various TYPES of online activities. Hamburger and Ben-Artzi (2000) Amiel & Sargent (2004) Some significant relationships found, e.g. extraversion and more social uses 33 Small / limited samples (e.g. experienced internet users) only selected measures of personality traits broad measures of internet use via self- EVALUATION report (e.g. work, social, informational) No reason for the relationships with personality As more uses of the internet transpire, it is unclear how personality traits will also predict these uses. 34 PERSONALITY AND SOCIAL NETWORKING AMCHAI-HAMBURGER & VINITZKY (2010) Research started to focus on social media and personality rather than just general internet behaviour Ross et al. (2009) found relationships but not strong, using self-report social media behaviour & NEO-PI-R (Costa & McCrae, 1992) Amichai-Hamburger & Vinitzky aim was to use objective measurements of user-information uploaded to Facebook 35 AMCHAI-HAMBURGER & VINITZKY (2010) METHODS User-information upload on Facebook was measured based on 4 dimensions basic information personal information contact information education and work information Categorised people as high and low on the 5 dimensions of personality 36 AMCHAI-HAMBURGER & VINITZKY (2010) RESULTS highly extraverted group had a larger number of friends but no differences in the use of Facebook groups Introverts transfer their pattern of behavior from the offline into the online world. This is reflected in the size of their social network which tends to be smaller than extraverts. However, introverts found to post more information on their profile than extraverts. This Photo by Unknown Author is licensed under CC BY-NC 37 AMCHAI-HAMBURGER & VINITZKY (2010) RESULTS Those scoring higher in neuroticism, were more willing to share personallyidentifying information on Facebook and less likely to use private messages. U shaped relationship between neuroticism and sharing of basic information. Those at High or low levels shared more basic information Those at Moderate shared lower levels of basic information. Different motivations for those high and low in neuroticism may result in similar increased level of sharing behaviour. 38 AMCHAI-HAMBURGER & VINITZKY (2010) CONCLUSIONS Overall, the results demonstrate a clear link of personality and Facebook use, with some similarities but some differences with earlier findings Ross et al.’s findings. Using actual Facebook profile data, Amichai-Hamburger & Vinitzky (2010), were able to demonstrate a stronger relationship between personality and Facebook behaviour than previous studies (e.g. Ross et al. 2009) 39 PERSONALITY AND SOCIAL NETWORKING - EVALUATION Personality still not a very strong predictor of the behaviour reported on social networking sites Do studies really examine online behaviour? – e.g. group size Some relationships are not linear and maybe mediated / moderated by other factors Not all personality dimensions are found to be consistently predictive of behaviour. e.g. Ross et al (2009) did not find any significant relationship between Facebook behaviour and the personality traits of agreeableness and openness. Some studies have compared personality traits for users vs non-users of social networking sites and started to examine narrow traits (e.g. Ryan & Xenos, 2011) Generalisability to other platforms and causal direction of relationships are potential issues 40 A TALE OF TWO SITES – HUGHES ET AL. (2012) Examined whether individual differences predict which SNS platform people use - Twitter vs Facebook Higher Sociability, Extraversion and Neuroticism – Preference for Facebook Higher Need for Cognition – Preference for Twitter Narrow traits, Sociability and need for cognition, were better predictors than Big 5 personality traits. Study could only account for between 10-20% of the variance in platform preference Shows individual differences related to specific platforms, but difficult to generalise to other platforms Each platform may do better to meet certain goals or perform certain functions 41 PREDICTING SMARTPHONE OPERATING SYSTEM FROM PERSONALITY AND INDIVIDUAL DIFFERENCES SHAW ET AL. ( 2016) Do individual differences exist between iPhone and Android users? iPhone owners were more likely to be female, younger, and increasingly concerned about their smartphone being viewed as a status object. model of smartphone ownership showed several IDs were reliable predictors. e.g. Gender strongest (2x more likely to own iPhones) although Age and extraversion type wereof notsmartphone owned provides some valuable information about its owner DIGITAL PHENOTYPING – SHAW ET AL. (2022) We have been discussing how Individual differences > Online behaviour Can also consider Data from digital devices > Individual differences / individuals Can potentially learn something about people or their characteristics based on data from their digital devices or their digital activities / footprint / traces Interpersonal and intrapersonal differences Privacy considerations? – Kosinski et al. (2016) MOTIVATIONS AND GRATIFICATIONS FROM ONLINE BEHAVIOUR 44 THE ROLE OF GOALS AND MOTIVATIONS ORCHARD & F ULLWOOD (20 10) – REVIEW Research shows personality predicts Internet behaviour as expected, with online behaviour related to offline personality. Individuals seek out activities that fit their predisposed needs. personality is also related to goals & motivations 45 MANY MOTIVATORS TO USE SOCIAL MEDIA – WILSON ET AL. (2012) A Review of Facebook Research in the Social Sciences – identified several motivations: External motivation – advertising, friends Internal motivation – social engagement, desire to keep in touch with friends. Strong and weak ties online Social capital - benefits received from relationships with other people Relieve boredom Alleviate loneliness Feel needed / liked / belong 46 ORCHARD & FULLWOOD (2010) - REVIEW High neuroticism scorers value CMC, choosing not to be involved in anxiety provoking experiences, such as online discussion groups, instead preferring blogging where there is greater control Psychoticism was linked to a strong avoidance in CMC and a preference for more socially unacceptable behaviours 47 ORCHARD & FULLWOOD (2010) Extraverts preferred environments that reflect their off-line identity, such as SNS. Introverts found to prefer online communication (specifically anonymous style interactions) because of the nature of CMC they can access the ‘real me’ online (e.g. Bargh, McKenna & Fitzsimmons, 2002) Both extraverts and introverts seem to benefit from using the internet but exploit the internet to get the most personal benefit 48 ORCHARD & FULLWOOD (2010) - REVIEW Evidence seems to support both the following hypotheses (cf., Zywica & Danowski, 2008) “poor-get-richer’’ (social compensation) those who perceive their offline social networks to be inadequate compensate for them with more extensive online social network ‘‘rich-get- richer’’ (social enhancement) those with more developed offline social networks enhance them with more extensive online social networks 49 ORCHARD & FULLWOOD (2010) – NARROW TRAITS The review also suggests that is not only broad personality traits that may be predictive of online behaviour narrow, specific personality traits can also predict internet usage and motives need for closure, locus of control, attachment style. The interaction of age and gender with personality is still unclear in how this predicts online behaviour Need to consider other models e.g. Uses and gratification paradigm (e.g. LaRose & Eastin, 2004) instead of just personality approach CONCLUSION May not be direct relationship between individual differences (such as personality) and behaviour Personality seems to predict motivations, and motivations seems to predict social media use (Orchard et al, 2014) CONTEXTUAL & SITUATIONAL EFFECTS ANONYMITY As well as individual differences, context / situational effects also have an impact on digitally connected behaviours One situational factor that has been shown to highly influence online behaviours is anonymity 53 Why does digitally connected behaviour vary across anonymous and identifiable environments? CONTEXT AND ANONYMITY ONLINE Two main theories try to explain the impact of anonymity on SNS behaviour Equalisation Hypothesis Social Identity Model of Deindividuation Effects (SIDE) EQUALISATION HYPOTHESIS In face-to-face interactions a person’s gender, age or ethnicity are apparent and therefore play a role in these social interactions Online, with the removal of many social cues, there is a reduction in stereotypes, and increased social power With increased anonymity and therefore reduced stereotypes, individuals who hold less power in society (F2F) should have increased power (more equality) in the online environment (via CMC). SOCIAL IDENTITY MODEL OF DEINDIVIDUATION EFFECTS (SIDE) Social Identity Theory states that we often categorise ourselves into social groups (Tajfel & Turner, 1974) The groups we identity with vary according to saliency Deindividuation is the loss of identity when in large crowds and, subsequently, less responsible behaviour Internet trolls are individuals who deliberately post negative messages online to provoke emotional responses. OTHER THEORETICAL APPROACHES TO EXPLAIN DIGITALLY CONNECTED BEHAVIOURS MODELS TO EXPLAIN DIGITALLY CONNECTED BEHAVIOURS Some research on digitally connected behaviour is limited in theoretical foundation (Orben, 2018, p12-14) But many models of behaviour / theories have been considered in how and why people adopt or use digital technologies, the internet, and social media FACTORS CONSIDERED SEPARATELY WHY Why do people adopt certain technologies What motivates people to use the technology Why do people continue to use technology What are they trying to achieve, or overcome What do they get out of using it Perceived Barriers, cost / benefits HOW What do they do with the technology How long do they use it for What activities do they engage in How do they interact with others WHO Other individuals that the user interacts or communicates with when using the technology The social norms of others relevant to the users (family and friends) and how motivated they are to comply with this WHAT What type of content the user experiences using the technology Particular models of behaviour focus on different parts of these questions but rarely do models WHY PEOPLE ADOPT OR CONTINUE TO USE TECHNOLOGY Technology acceptance model (TAM) theory that predicts technology adoption and future-use of technology (Marangunic & Granic, 2015) Includes perceived usefulness, perceived ease of use, attitudes etc Only considers technology adoption Technology Integration Model (TIM) Theory also includes variables related to continued technology use as well as adoption (Shaw et al. 2018) KAYE (2021, P.17) MODEL OF FACTORS RELEVANT TO UNDERSTANDING TECHNOLOGY USE Adoption Software Affordanc es Engageme nt Perceptions Hardwar e Continued Behaviours & interaction s 61 OTHER MODELS / THEORIES THAT CAN BE USED TO PREDICT ONLINE BEHAVIOUR Uses and gratification (e.g. Katz, Blumler, & Gurevitch, 1974; LaRose & Eastin, 2004) Basic Psychological Needs Theory (BPNT) - Selfdetermination theory (e.g. Deci & Ryan, 2000) Theory of Reasoned Action / Theory of Planned Behaviour (TRA / TPB) (e.g. Azjen, 1991) 62 OTHER MODELS / THEORIES THAT CAN BE CONSIDERED General Aggression model: GAM) (e.g. Anderson & Bushman, 2002) e.g. Video game violence Health belief model (e.g. Rosenstock, 1974) e.g. cybersecurity Behaviour change theories – COM-B (Michie, van Stralen , & West, 2011) capability, motivation, opportunity CONCLUSIONS Unlikely that people use digital technology and the internet differently just on the basis of the generation they belong to. Specific groups may use the internet differently due to different motives or digital platforms, but little evidence of a consistent digital age divide. Likely needs met through other means Consider digital literacy CONCLUSIONS Some personality traits show some consistent relationship with particular online behaviours, in particular extroversion, but difficult mix of results to interpret across studies Personality still only explains small % of variance in behavior online. May be highly platform dependent Narrow predictors are sometimes better than broad personality traits at predicting what people will do online. E.g. Internet self-efficacy, shyness, need to belong, loneliness, narcissism Situational / contextual factors such as anonymity can also have a large influence on online behaviours CONCLUSIONS Relationships may not be linear and there may be interaction of individual differences. Still unclear the mediating / moderating effects of age and gender. Both extraverts and introverts use the internet differently, but both seem to benefit. May be beneficial to also consider / compare how other theoretical constructs or models of behaviour can predict online / digitally mediated behaviours. CONCLUSIONS & EVALUATION Many studies have struggled to define ‘online behaviour’ Especially as some only measure number of friends or level of usage which are not behaviours May need more nuanced taxonomy of online behaviour CONCLUSIONS & EVALUATION The more general the definition the more meaningless, but the more specific the less generalizable Difficult to generalise across platforms Internet is changing and therefore harder to define discrete uses of the internet CONCLUSIONS & EVALUATION Many studies use small or homogenous samples, therefore hard to generalise self report usage data may not reflect actual behaviour (e.g. Ellis et al., 2019) Self report data is overestimated (e.g. Parry et al. 2020) but objective measures of online behaviours are starting to be more readily available in for research 69 READING Please see the relevant unit on Canvas for the required and further reading for this lecture