International Journal of Interactive Mobile Technologies(iJIM) – eISSN: 1865-7923 – Vol 16 No 15 (2022) Paper—Factors Influencing Users’ Satisfaction Towards Image Use in Social Media: A PLS-SEM Analysis Factors Influencing Users’ Satisfaction Towards Image Use in Social Media: A PLS-SEM Analysis https://doi.org/10.3991/ijim.v16i15.32555 Irma Syarlina Che Ilias1,2, Suzaimah Ramli1(*), Muslihah Wook1, Nor Asiakin Hasbullah1 1Department of Computer Science, Faculty of Defense Science and Technology, National Defence University of Malaysia, Selangor, Malaysia 2Computer Engineering Technology Section, Malaysian Institute of Information Technology, Universiti Kuala Lumpur, Kuala Lumpur, Malaysia suzaiman@upnm.edu.my Abstract—Images for social media are an essential component of any blog or social media post. As we now live in the age of the ‘camera in every- one’s pocket’, a new dynamic era of image creation and content has emerged. ­However,­people­use­media­due­to­specific­motives­or­gratifications­that­lead­ to their satisfaction of social media use. This research study utilized uses and gratification­theory­(UGT),­a­sociology­theory­related­to­the­motives­of­peo- ple­using­the­media.­The­UGT­factors­include­enjoyment,­entertainment,­social­ influence,­social­interaction,­and­information­sharing.­There­were­441­data­that­ were collected and analysed. IBM SPSS Statistics 20 and Smart PLS 3.3.3 were used for the data analysis. Result showed that information sharing was the most influencing­­factors­on­users’­satisfaction­to­use­image­in­social­media.­Therefore,­ the­factors­identified­in­these­studies­could­be­used­as­a­guideline­and­references­ in future study related to social media awareness and implications. Keywords—uses­and­gratification­theory,­satisfaction,­image­use,­social­media 1 Introduction Social media are the incorporation of digital media, such as images into a digital environment that allows people to interact with data for a variety of purposes. Com- pared to others, images produce higher levels of interaction because images are the attributes to the audience’s viewing time, easily shared and constantly used in various social­media­platforms.­With­the­knowledge­of­motive­or­gratification,­images­are­used­ across most social media platform where users carefully craft the way images are per- ceived by others and engage to use in the social media [1]. A study by Norsharina et al. [2], suggest future research to look into reason for using social media platforms. According to Ajis et al. [3],­based­on­Uses­and­Gratification­ Theory­(UGT),­people­are­using­social­media­due­to­motive­that­provide­satisfaction­ and­fulfil­their­needs.­In­addition,­Gan­et­al.­[4]­showed­that­satisfaction­affected­users’­ behavior to continue using social media. There are also many theories have been pro- posed to explain users’ acceptance and intention on the social media [5]. 172 http://www.i-jim.org https://doi.org/10.3991/ijim.v16i15.32555 mailto:suzaiman@upnm.edu.my Paper—Factors Influencing Users’ Satisfaction Towards Image Use in Social Media: A PLS-SEM Analysis Since­motive­or­gratification­provide­satisfaction­to­user’s­needs,­this­study­aimed­ to­elucidate­the­factors­influencing­users’­satisfaction­on­image­use­in­social­media.­ ­Considering­ prior­ studies­ [6]–[10],­ we­ identified­ and­ explored­ five­ UGT­ factors:­ ­enjoyment,­entertainment,­social­influence,­social­interaction­and­information­sharing.­ Based­on­our­knowledge,­this­is­the­first­study­that­reports­on­users’­satisfaction­special- ized on image use in social media. 2 Literature review and hypothesis development 2.1 Image use in social media Rogers [11] discussed that there was an evolution on image use. It started with digital images during the 1990s, networked images on 2000s and image use in social media since 2010s. Social media provide a platform to upload photos based on users’ choice as­profile­picture,­cover­photo­and­many­more­[12].­Furthermore,­photo­as­image­holds­ a dominant position among the various types of content shared and viewed on social media, becoming increasingly important across all major social media platforms [13]. 2.2 Uses and gratification theory The­theory­of­UGT­explains­how­people­use­media,­and­investigates­the­various­ gratifications­that­drive­media­use­[14].­UGT­emphasizes­the­various­effects­of­gratifi- cations on people’s motivation to engage in behaviors [15]. Moreover, several research studies­used­UGT­to­examine­and­explore­why­individuals­used­social­media­and­the­ benefits­gained­when­consuming­media,­connecting,­and­also­exchanging­content­and­ knowledge from those media using various media [16]. Users will continue to interact with social media if their satisfaction and desires are in line with the platform [17]. In addition,­according­to­Kujur­and­Singh­[18],­UGT­is­well-adapted­to­investigate­the­ effects and factors of visual communications and using such images on social media. Therefore,­the­diversity­of­the­usage­of­social­media­is­expressed­in­the­UGT­study. Enjoyment: Yan Li [6] indicated that social media could offer various forms of enjoyment. A strong relationship between enjoyment and social media use has been advocated by a great deal of empirical evidence. Likewise, the use of social media is expected to meet users’ needs to enjoy and enhance their inner satisfaction. Therefore, this study hypothesized: H1: User’s satisfaction with the images is positively affected by enjoyment. Entertainment:­Entertainment­is­the­final­pleasure­discovered­to­be­correlated­with­ social media activities to relieve boredom or just to have fun [19]. Entertainment is important in relation to the popularity of social media content, indicating that users use social media as a means of entertainment [7]. Therefore, this study hypothesized: H2: User’s satisfaction with the images is positively affected by entertainment. iJIM ‒ Vol. 16, No. 15, 2022 173 Paper—Factors Influencing Users’ Satisfaction Towards Image Use in Social Media: A PLS-SEM Analysis Social Influence:­Social­influence­captures­the­perceived­expectations­of­individu- als­or­groups­of­specific­referents­of­a­person,­and­his­or­her­motivation­to­meet­these­ expectations [20]. Disciplines such as Information Systems (IS) have been included in the­study­of­the­social­influence­of­social­circles­in­the­digitised­society­[8].­Therefore,­ this study hypothesized: H3:­User’s­satisfaction­with­the­images­is­positively­affected­by­social­influence. Social Interaction: The­social­interaction­refers­to­the­benefits­of­socialising­with­ other­media­users­and­it­has­been­shown­to­be­a­great­significance­in­studies­on­media­ user [21]. In addition, social media are a type of medium that allows for interactive or two-way social interactions, shifting the pattern of information dissemination from one to many audiences [9]. Therefore, this study hypothesized: H4:­User’s­satisfaction­with­the­images­is­positively­affected­by­social­interaction. Information Sharing: Information sharing correlates with social media interactions and communications with members to increase and satisfy a user’s satisfaction with a social media platform [22]. It also entails status changes and public posts, and is a vehicle for a higher proportion of information sharing [23]. Therefore, this research study hypothesized: H5: User’s satisfaction with the images is positively affected by information sharing. 3 Materials and method This section contains information on the study method, data collection, the instru- ment used, the pilot study, the study sample, and demographic data. 3.1 Research method The­quantitative­research­method­was­used­to­measure­the­factors­that­influenced­ image­use­in­social­media.­The­study­population­comprised­of­67,634­students­of­11­ MARA Education Institute (IPMA) in various states in Malaysia. However, it is possible and­ difficult­ to­ capture­ all­ elements­ (individuals­ or­ items)­ in­ the­ target­ ­population­ because each research study has limitations and constraints, such as time, energy, cost and area distance [23], [25]. Therefore, the accessible population of IPMA were chosen from two states, Selangor and Kuala Lumpur. Four chosen IPMA within both states also represented the two groups of IPMA, namely KPTM and KUPTM from the Higher Education group, and GiatMARA­and­UniKL­from­TVET­institution­group.­Since­the­elements­in­the­popula- tion were known [26], this study was based on complex probability sampling, a select- ing­stratified­sampling­technique­for­sample­selection­based­on­the­IPMA­group­types. As suggested by Memon et al. [27], since PLS-SEM was used to analysed the con- ceptual models, this study considered two methods suggested by PLS-SEM to estimate the appropriate sample size: 174 http://www.i-jim.org Paper—Factors Influencing Users’ Satisfaction Towards Image Use in Social Media: A PLS-SEM Analysis • A­priori­analysis­using­G­Power­software­[28],­[29]. • The sample size table by Krejcie & Morgan [30], [31]. The­minimum­sample­sizes­obtained­from­the­priori­analysis­using­G­Power­­software­ and the sample size tables were 199 and 377, respectively. Although each method yielded a minimum size value, this study chose the minimum sample size of the highest value (377) because according to Taherdoost [32], the higher the sample size value, the less sampling error. Moreover, high sample sizes are good because they provide greater­reliability­to­the­study­findings,­and­enable­more­complex­statistical­analysis­ [33].­[34]­have­recommend­trying­the­methods­on­larger­samples. 3.2 Data collection As a data collection tool, a questionnaire was created. The questionnaire was divided into two sections: demographic information and questions about each factor. Further- more,­8­and­30­items­were­evaluated­to­determine­the­factors­that­influenced­the­use­ of images on social media. All items in this study were rated on a 7-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). The validity and reliability of the measurement instrument were checked using a pilot study with a small-scale study of a group of respondents who had similar characteristics to the actual survey popula- tion [26], [35]. The­pilot­study­on­Cronbach’s­alpha­was­carried­out­with­40­students,­and­the­Cron- bach’s alpha for all six constructs exceeded the cut-off point of 0.70 [36]. As a result, in the actual survey, all of the constructs were employed and were successfully answered by 530 respondents. However, those who submitted incomplete data were excluded, yielding­441­usable­surveys. 3.3 Data analysis There are two methods of data analysis conducted: descriptive analysis and struc- tural equation modelling analysis. Both were conducted after the preliminary data anal- ysis which showed 89 surveys were removed through data cleaning. 3.4 Research model The­association­between­UGT­factors­and­users’­satisfaction­with­the­images­used­ on social media is represented as research model in Figure 1 based on the literature review and proposed hypotheses. As suggested by Rautela et al. [37], research model can be empirically tested to strengthen the model developed. Figure 1 illustrates that the research model of the factors for users’ satisfaction consists of enjoyment (EN), entertainment­(ET),­social­influence­(SF),­social­interaction­(ST)­and­information­shar- ing (IS). In total, there were 30 items to measure the proposed model and hypothesis. iJIM ‒ Vol. 16, No. 15, 2022 175 Paper—Factors Influencing Users’ Satisfaction Towards Image Use in Social Media: A PLS-SEM Analysis Fig. 1. Research model – measurement and structural model 176 http://www.i-jim.org Paper—Factors Influencing Users’ Satisfaction Towards Image Use in Social Media: A PLS-SEM Analysis 4 Results 4.1 Descriptive analysis IBM SPSS Statistic 20.0 was used to run the demographic and construct analysis. Table 1. Descriptive analysis of demographic variables No Demographic Variable Category Frequency Percentage (%) 1 Gender Male 181 41% Female 260 59% 2 Age Below 17 2 0.5% 18 to 20 230 52.2% 21 to 22 160 36.3% Above­24 49 11.1% 3 IPMA Type GIAT­MARA 7 1.6% KPTM 115 26.1% KUPTM 97 22.0% UniKL 222 50.3% 4 IPMa location Kuala Lumpur 284 64% Selangor 157 36% 5 IPMa program level Certificate 53 12.0% Diploma 174 39.5% Bachelor’s degree 214 48.5% 6 Most active social media accounts used. Facebook 68 15.4% Instagram 201 45.6% Tik Tok 97 22.0% Twitter 75 17.0% 7 Most preferred information types used on social media Audio 37 8.4% Image 172 38.9% Text 86 19.6% Video 146 33.1% 8 Most preferred places where images are used on social media Album 54 12.4% Comment 37 8.4% Cover photo 52 11.7% Post 113 25.6% Profile­photo 92 20.9% Story 93 21.0% Based on the descriptive analysis shown in Table 1, the gender breakdown of the respondents­indicated­that­women­accounted­for­260­(59%)­of­the­total­respondents­ while­men­were­only­181­(41%).­The­majority­of­the­respondents­were­between­18­to­ 23­years­old­(230,­52.2%)­and­most­of­them­were­from­bachelor­program­(214,­48.5%).­ iJIM ‒ Vol. 16, No. 15, 2022 177 Paper—Factors Influencing Users’ Satisfaction Towards Image Use in Social Media: A PLS-SEM Analysis Compared­to­the­other­IPMAs,­UniKL­students­had­larger­respondents­(222,­50.3%)­ of­total­respondents­and­IPMA­in­Kuala­Lumpur­had­greater­respondents­(284,­64%).­ Instagram­was­the­most­active­social­media­accounts­used­by­respondents­(201,­45.6%)­ and­172­(38.9%)­respondents­preferred­to­use­image­on­social­media­compared­to­other­ information­types.­The­results­of­the­analysis­also­showed­Post­(25.6%),­Story­(21.0%),­ Profile­photo­(20.9%)­and­Album­(12.4%)­were­among­most­preferred­places­where­ respondents used images on social media. Table 2 shows the results of the construct analysis for all the six constructs in the research study. The results of the analysis showed that the constructs enjoyment (EN), entertainment (EN) and information sharing (IS) had at high score level among the respondents. The rest of the constructs got scale scores at a medium level. In addition, the standard deviation showed a small value, meaning that the study data were not spread far beyond the mean value [38]. Table 2. Descriptive analysis of construct No Construct Mean Standard Deviation Scale Score 1 Enjoyment (EN) 5.268 0.991 High 2 Entertainment (ET) 5.011 1.126 High 3 Social­Influence­(SF) 4.486 1.192 Medium 4 Social Interaction (ST) 4.938 1.261 Medium 5 Information Sharing (IS) 5.362 1.093 High 6 Satisfaction (SA) 4.600 1.338 Medium 4.2 Structural equation modelling analysis SmartPLS 3.3.3 Statistical Tool was used to run the measurement and structural model. The research study used a factor analysis to test the validity and reliability of each item used in the research model in order to test Hypotheses 1–5. It was necessary to validate the measurement models for the constructs that were constrained to the same loadings. The results of the measurement models are shown in Table 3. The composite reliability (CR) is estimated to estimate reliability, with a CR of 0.8 or greater deemed acceptable for research study [39]. Table 3 shows that all constructs exceed the cut-off value of 0.8, indicating that all items support the constructs’ internal consistency. To assess the constructs’ convergent validity, two approaches, the items’ cross load- ings­and­average­variance­extracted­(AVE),­for­each­construct­were­used.­The­analysis­ showed­four­items­were­below­0.7,­and­they­were­deleted;­EN4,­EN5,­ET5­and­SF5.­ As­suggested­by­Reinartz­[40],­item­with­low­loading­can­be­eliminated­to­substantially­ increase­AVE­and­CR.­The­total­items­deleted­were­13.33%­which­were­less­than­20%­ of­the­total­items­that­were­strongly­advised­to­not­be­deleted­[41]. According­to­Chin­(1998),­an­AVE­greater­than­0.5­was­considered­statistically­sig- nificant.­The­AVE­value­of­each­construct­in­this­study­was­greater­than­0.5.­The­square­ root­of­the­AVE­for­each­construct­and­the­correlation­involving­the­construct­were­ evaluated to assess discriminant validity. According to Fornell C & Larcker FD [39], the­square­root­of­the­AVE­for­each­construct­must­be­greater­than­the­correlations­ 178 http://www.i-jim.org Paper—Factors Influencing Users’ Satisfaction Towards Image Use in Social Media: A PLS-SEM Analysis that include the constructs. The results demonstrated that the discriminant validity was acceptable.­Therefore,­all­of­the­constructs­were­sufficiently­reliable­and­valid­to­test­ the hypotheses. In Structural Model Analysis, it is critical to ensure that there is no lateral collinear- ity­in­the­structural­model­before­evaluating­it­[43].­The­lateral­collinearity­test­results­ are­shown­in­Table­3.­All­of­the­Inner­VIF­values­for­the­five­UGT­factors­that­needed­ to be investigated for lateral multicollinearity were less than 5, indicating that lateral multicollinearity was not a concern in the research study [36]. Table 3. Lateral collinerity assessment Construct Satisfaction (SA) Enjoyment (EN) 1.972 Entertainment (ET) 2.663 Information Sharing (IS) 2.123 Social­Influence­(SF) 2.122 Social Interaction (ST) 2.893 In­this­study,­five­direct­hypotheses­between­independent­and­dependent­constructs­ were­developed.­Table­5­shows­that­all­relationships­have­a­T-value­of­1.645­and­are­ significant­since­P-value<0.05.­As­a­result,­all­hypotheses­were­accepted.­The­R2 value of 0.630 was greater than the 0.26 value suggested by Cohen [33], indicating that this research­model­was­significant­and­accepted.­The­change­in­R2 value, as proposed by Hair­Jr­et­al.­[44],­should­also­be­examined­and­reported.­Cohen­[33],­guidelines­were­ used to measure effect size, with values of 0.02, 0.15, and 0.35 representing small, medium,­and­large­effects,­respectively.­Table­5­demonstrates­that­all­UGT­constructs­ had a small effect on R2 for Satisfaction. In addition, the predictive relevance of the research model was tested using a blind- folding­procedure.­Hair­Jr­et­al.­[44],­stated­that­if­the­Q2 value was greater than zero, the model­had­predictive­relevance­for­a­dependent­construct.­The­Q2 value for Satisfaction was greater than zero, indicating that the model had an adequate predictive relevance. Furthermore,­a­reflective­measure­of­predictive­relevance,­the­values­of­0.02,­0.15,­and­ 0.35 indicated that an independent construct had a small, medium, or large predictive relevance­for­a­specific­dependent­construct,­respectively.­The­results­showed­small­q2 effect size for EN, ET, SI and IS constructs on satisfaction while no q2 effect size was detected for SF construct on satisfaction. Figure­2­presents­the­final­research­model­on­evaluating­the­influence­factors­on­ users’­satisfaction­to­use­image­in­social­media.­The­figure­shows­the­summary­from­ Tables­4­and­5,­between­inner­model­(Path­Coefficient­or­Standard­Beta),­outer­model­ (Cross Loadings) and dependent construct (R2). iJIM ‒ Vol. 16, No. 15, 2022 179 Paper—Factors Influencing Users’ Satisfaction Towards Image Use in Social Media: A PLS-SEM Analysis Fig. 2. Research model – measurement and structural model 180 http://www.i-jim.org Paper—Factors Influencing Users’ Satisfaction Towards Image Use in Social Media: A PLS-SEM Analysis Ta bl e 4. M ea su re m en t m od el a na ly si s C on st ru ct C ro ss L oa di ng C om po si te R el ia bi lit y AV E C ro nb ac h’ A lp ha C or re la ti on o f C on st ru ct s E N E T IS SA SF ST E nj oy m en t ( E N ) E N 1 0. 88 4 0. 89 2 0. 73 4 0. 81 8 0. 85 7 E N 2 0. 79 3 E N 3 0. 88 9 E nt er ta in m en t ( E T ) E T 1 0. 90 6 0. 94 0. 79 7 0. 91 4 0. 68 4 0. 89 3 E T 2 0. 91 4 E T 3 0. 93 6 E T 4 0. 80 9 In fo rm at io n Sh ar in g (I S) IS 1 0. 85 9 0. 93 1 0. 73 0 0. 90 7 0. 52 8 0. 60 3 0. 85 4 IS 2 0. 88 5 IS 3 0. 79 2 IS 4 0. 87 2 IS 5 0. 88 4 Sa tis fa ct io n SA 1 0. 88 2 0. 93 9 0. 75 5 0. 91 9 0. 59 0 0. 69 0 0. 66 5 0. 86 9 SA 2 0. 90 2 SA 3 0. 81 8 SA 4 0. 89 7 SA 5 0. 84 2 So ci al ­In flu en ce ­ (S F) SF 1 0. 84 8 0. 92 1 0. 74 4 0. 88 6 0. 43 5 0. 60 9 0. 51 3 0. 59 4 0. 86 3 SF 2 0. 88 7 SF 3 0. 87 5 SF 4 0. 84 So ci al In te ra ct io n (S T ) ST 1 0. 91 3 0. 94 9 0. 78 7 0. 93 2 0. 52 9 0. 64 8 0. 69 1 0. 70 4 0. 69 0. 88 7 iJIM ‒ Vol. 16, No. 15, 2022 181 Paper—Factors Influencing Users’ Satisfaction Towards Image Use in Social Media: A PLS-SEM Analysis Table 5. Hypothesis testing Hypothesis Standard Beta Standard Error T-Value P-Value R2 f2 Q2 q2 H1 0.129 0.050 2.573 0.005 0.629 0.022 0.467 0.011 H2 0.240 0.063 3.837 0.000 0.059 0.032 H3 0.096 0.056 1.718 0.043 0.011 0.006 H4 0.259 0.057 4.556 0.000 0.060 0.028 H5 0.224 0.052 4.314 0.000 0.068 0.036 5 Discussion and future studies Firstly,­the­result­showed­that­all­UGT­factors,­namely­enjoyment­(EN),­entertain- ment­(ET),­social­influence­(SF),­social­interaction­(ST)­and­information­sharing­(IS)­ were­significant,­and­all­the­factors­influenced­users’­satisfaction­on­using­image­in­ social­media.­Information­Sharing­was­the­most­influencing­factors.­This­is­supported­ by Liu et al. (2019). Information sharing represents the extent to which image as social media content allows users to convey and spread information, share interest with oth- ers, increase users’ social connections and be useful to others. Secondly, the R2 value calculated represented the combined effects of independent variables­ (UGT­ factors)­ on­ dependent­ variable­ (Satisfaction)­ and­ showed­ that­ the­ model had substantial level of predictive accuracy. This is supported by Hair Jr et al. [36],­[44].­Hence,­the­Users’­satisfaction­with­image­use­in­social­media­was­deter- mined­by­Enjoyment,­Entertainment,­Social­Influence,­Social­Interaction­and­Informa- tion Sharing that could be derived from image as the content in social media. Finally, the items measured were consistent with what it intended to measure. The loading values for each item were greater than 0.708, indicating that each variable could­explain­at­least­50%­of­the­variance­for­that­item.­This­is­supported­by­Hair­Jr­ et­al.­[44].­Therefore,­there­are­26­items­that­can­be­used­as­instrument­to­study­image­ use in social media while four items could be further improved. Although this study will be useful in both academic research and managerial appli- cations, future studies should pay attention to a few issues. This study primarily col- lected data from IPMA, an educational institution under the government agencies, and our­findings­may­not­be­generalizable­to­other­populations.­In­addition,­this­research­ model­focused­on­factors­influencing­users’­satisfaction.­Therefore,­the­model­could­ be extended to user continuance intention and behaviour to use image in social media. Quantitative­method­was­used;­hence,­a­mix­method­or­qualitative­method­could­be­ used­to­explore­different­approaches­and­findings. 6 Conclusion This­study­investigated­the­key­considerations­or­factors­influencing­social­media­ users’­satisfaction­towards­image­as­content.­Five­UGT­factors­were­part­of­research­ model­developed­and­analyzed.­Overall,­the­study­found­that­motive­or­gratification­ factors played a role as consideration factors prior to satisfaction such as Information 182 http://www.i-jim.org Paper—Factors Influencing Users’ Satisfaction Towards Image Use in Social Media: A PLS-SEM Analysis Sharing,­the­most­influencing­factors.­Hence,­a­substantial­level­of­predictive­accuracy­ of the research model was shown based on the R2­value­(0.629)­within­the­UGT­factors­ and user satisfaction. However, there were four instrument or items which were deleted due to the load- ing­values­requirement,­namely­EN4,­EN5,­ET5­and­SF5.­Therefore,­by­revising­the­ deleted instrument or items, we were able to improve and strengthen the research model. Ultimately, this study shows that choosing image as content comes with a vast array­of­motive­or­gratification­which­is­not­easily­able­to­satisfy­users. 7 Acknowledgment The authors gratefully acknowledge Universiti Pertahanan Nasional Malaysia, and the­Short-Term­Research­Grant,­code­UPNM/2020/GPJP/ICT/2. 8 References ­ [1]­E.­Lowe-Calverley­and­R.­Grieve,­“Thumbs­up:­A­thematic­analysis­of­image-based­posting­ and liking behaviour on social media,” Telemat. Informatics, vol. 35, no. 7, pp. 1900–1913, 2018, https://doi.org/10.1016/j.tele.2018.06.003 ­ [2]­N.­Zabidi­and­W.­Wang,­“The­use­of­social­media­platforms­as­a­collaborative­supporting­ tool: A preliminary assessment,” Int. J. Interact. Mob. 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She is currently a PhD student in Computer Science at National Defence University of Malaysia. She has widely experience in computer networking and multimedia system since 2001. She also has been recognized as professional technologist by the Malaysian Board of Technologist. Her research interests include visual informatics, human behavior and social networks in computer technology. iJIM ‒ Vol. 16, No. 15, 2022 185 https://doi.org/10.1177/001316447003000308 https://doi.org/10.1080/23311886.2022.2027613 https://doi.org/10.3991/ijim.v12i4.9201 https://doi.org/10.3991/ijim.v14i03.10490 https://doi.org/10.3991/ijim.v14i03.10490 https://doi.org/10.1007/978-981-15-2537-7_5 https://doi.org/10.1177/002224378101800104 https://doi.org/10.1177/002224378101800104 https://doi.org/10.1016/j.ijresmar.2009.08.001 https://doi.org/10.1504/IJMDA.2017.087624 https://doi.org/10.1504/IJMDA.2017.087624 Paper—Factors Influencing Users’ Satisfaction Towards Image Use in Social Media: A PLS-SEM Analysis Suzaimah Ramli received her PhD in Electrical, Electronic and System Engineering from Universiti Kebangsaan Malaysia in 2011, Master of Computer Science from Universiti Putra Malaysia in 2001, and Bachelor of Information Technology (Hons) from Universiti Utara Malaysia in 1997. Her research interests include image process- ing­and­artificial­intelligence­applications­in­various­domain­particularly­in­education,­ security and defence. Currently she is working as an Associate Professor at Department of Computer Science, Faculty of Defence Science and Technology, National Defence University of Malaysia. She is a member of Malaysia Board of Technologist and ­Informatics­ Intelligence­ Special­ Interest­ Group,­ UPNM.­ She­ has­ published­ and­ ­presented­most­of­her­research­findings­to­various­international­conferences­and­articles­ in­many­­international­journals­specifically­in­her­research­niche. Muslihah Wook received her PhD in Information Science from Universiti Kebangsaan Malaysia in 2017, Master of Computer Science from Universiti Putra Malaysia­in­2004,­and­Bachelor­of­Information­Technology­(Hons)­from­Universiti­ Utara Malaysia in 2001. Her research interests include big data analytics and data mining applications in various domain particularly in education, security and defence. Cur- rently she is working as a Senior Lecturer at Department of Computer Science, Faculty of Defence Science and Technology, National Defence University of Malaysia. She has become members of International Association of Computer Science and Information Technology (IACSIT) and Institute of Research Engineers and Doctors (IRED) since 2011 and 2013 respectively. Recently, she has been appointed as a technical reviewer of Education and Information Technologies and Journal of Big Data—Springer journals indexed­by­ISI­and­Scopus­(Q1),­and­other­outstanding­journals­as­well. Nor Asiakin Hasbullah is currently a Senior Lecturer at the Department of Computer Science, National Defence University of Malaysia. Asiakin holds a Ph.D. in IT and Quantitative­Sciences­(System­Sciences)­and­Master­of­Science­in­Information­Tech- nology from the MARA University of Technology Malaysia and Bachelor Degree of Science in Information Technology (Hons.) from the National University of Malaysia. Her­research­interest­and­expertise­are­in­the­field­that­covers­intersection­between­ society and technology such as information privacy, ethics in ICT, social informatics and social network behaviour. She is also a professional technologist, recognition given by the Malaysian Board of Technologist. Article­ submitted­ 2022-04-19.­ Resubmitted­ 2022-05-24.­ Final­ acceptance­ 2022-05-25.­ Final­ version­ published as submitted by the authors. 186 http://www.i-jim.org