With the increasing of online social behavior, social relationships have an important impact on consumer negative comment behavior (CNCB) on social commerce platforms. Existing studies lack to describe CNCB influenced by social relationships on social commerce platforms from the perspective of well-thought-out planning results, and the proposed structural equation models in previous studies have been difficult to predict CNCB. Hence, this study proposes a new structural equation model (SEM) and artificial neural network (ANN) model to deeply explore and reveal the generation mechanism of CNCB in the context of social commerce platforms based on the theory of planned behavior (TPB). We regard social support as a moderating effect and construct a consumer negative comment planning behavior model (CNCPBM). The results of the data analysis show CNCPBM is supported. This study provides an important theoretical and practical contribution to CNCB, and offers practical management enlightenment for the managers of social commerce platforms.
LiuY., GanW.X. and ZhangQ., Decision-making mechanism of online retailer based on additional online comments of consumers, Journal of Retailing and Consumer Services59 (2021), 102389.
2.
Al-AdwanA.S., Al-DebeiM.M. and DwivediY.K., E-commerce in high uncertainty avoidance cultures: The driving forces of repurchase and word-of-mouth intentions, Technology in Society71 (2022), 102083.
3.
MardhiyahD. and DharmmestaB.S., Antecedents to intention to engage in the online negative word-of-mouth communication, Gadjah Mada International Journal of Business15(2) (2013), 113–132.
4.
LiK., ChenY. and ZhangL., Exploring the influence of online reviews and motivating factors on sales: A meta-analytic study and the moderating role of product category, Journal of Retailing and Consumer Services55 (2020), 102107.
5.
SunY., Gonzalez-JimenezH. and WangS., Examining the relationships between e-WOM, consumer ethnocentrism and brand equity, Journal of Business Research130 (2021), 564–573.
6.
LiY., WuR. and LiD., The influence of subjective characteristics of social network sites on consumers’ word-of-mouth sharing, Online Information Review44(5) (2020), 977–994.
7.
LangfordC.P.H., BowsherJ., MaloneyJ.P. and LillisP.P., Social support: a conceptual analysis, Journal of Advanced Nursing25(1) (1997), 95–100.
8.
AliY.S., HussinA.R.C. and DahlanH.M., Electronic Word of Mouth engagement in social commerce platforms: An empirical study, Information Development36(3) (2020), 438–456.
9.
BusalimA.H. and GhabbanF., Customer engagement behaviour on social commerce platforms: An empirical study, Technology in Society64 (2021), 101437.
10.
WangJ.L., JacksonL.A., WangH.Z. and GaskinJ., Predicting social networking site (SNS) use: Personality, attitudes, motivation and internet self-efficacy, Personality and Individual Differences80 (2015), 119–124.
11.
YenY.S., Factors enhancing the posting of negative behavior in social media and its impact on venting negative emotions, Management Decision54(10) (2016), 2462–2484.
12.
DoanhD.C. and BernatT., Entrepreneurial self-efficacy and intention among Vietnamese students: A meta-analytic path analysis based on the theory of planned behavior, Procedia Computer Science159 (2019), 2447–2460.
13.
PoveyR., ConnerM., SparksP., JamesR. and ShepherdR., The theory of planned behaviour and healthy eating: Examining additive and moderating effects of social influence variables, Psychology & Health14(6) (2000), 991–1006.
14.
FuJ.R., JuP.H. and HsuC.W., Understanding why consumers engage in electronic word-of-mouth communication: Perspectives from theory of planned behavior and justice theory, Electronic Commerce Research and Applications14(6) (2015), 616–630.
15.
LeA.N.H. and HoH.X., The behavioral consequences of regret, anger, and frustration in service settings, Journal of Global Marketing33(2) (2020), 84–102.
16.
BarariM., RossM. and SurachartkumtonkunJ., Negative and positive customer shopping experience in an online context, Journal of Retailing and Consumer Services53 (2020), 101985.
17.
ShahzalalM. and FontX., Influencing altruistic tourist behaviour: Persuasive communication to affect attitudes and self-efficacy beliefs, International Journal of Tourism Research20(3) (2018), 326–334.
18.
HussainS., GuangjuW., JafarR.M.S., IlyasZ., MustafaG. and JianzhouY., Consumers’ online information adoption behavior: Motives and antecedents of electronic word of mouth communications, Computers in Human Behavior80 (2018), 22–32.
19.
WangX., LinX. and SpencerM.K., Exploring the effects of extrinsic motivation on consumer behaviors in social commerce: Revealing consumers’ perceptions of social commerce benefits, International Journal of Information Management45 (2019), 163–175.
20.
WakefieldL.T. and WakefieldR.L, Anxiety and ephemeral social media use in negative eWOM creation, Journal of Interactive Marketing41 (2018), 44–59.
21.
MolinilloS., Anaya-SánchezR. and Liebana-CabanillasF., Analyzing the effect of social support and community factors oncustomer engagement and its impact on loyalty behaviors towardsocial commerce websites, Computers in Human Behavior108 (2020), 105980.
22.
BaiY., YaoZ. and DouY.F., Effect of social commerce factors on user purchase behavior: An empirical investigation from renren. Com, International Journal of Information Management35(5) (2015), 538–550.
23.
LiangX., HuX., IslamT. and MubarikM.S., Social support, source credibility, social influence, and solar photovoltaic panels purchase intention, Environmental Science and Pollution Research28(41) (2021), 57842–57859.
24.
SheikhZ., YezhengL., IslamT., HameedZ. and KhanI.U., Impact of social commerce constructs and social support on social commerce intentions, Information Technology & People32(1) (2019), 68–93.
25.
HuX., ChenX. and DavisonR.M., Social support, source credibility, social influence, and impulsive purchase behavior in social commerce, International Journal of Electronic Commerce23(3) (2019), 297–327.
26.
WangT., YehR.K.J., ChenC. and TsydypovZ., What drives electronic word-of-mouth on social networking sites? Perspectives of social capital and self-determination, Telematics and Informatics33(4) (2016), 1034–1047.
27.
HajliM.N., The role of social support on relationship quality and social commerce, Technological Forecasting and Social Change87 (2014), 17–27.
28.
BalajiM.S., KhongK.W. and ChongA.Y.L., Determinants of negative word-of-mouth communication using social networking sites, Information & Management53(4) (2016), 528–540.
29.
XuH., ZhangK.Z. and ZhaoS.J., A dual systems model of online impulse buying, Industrial Management & Data Systems120(5) (2020), 845–861.
30.
DuttaN., VermaA. and BhatA., Analysis of factors influencing negative e-word of mouth in online social media marketing, In Proceedings of the 2019 3rd International Conference on Software and e-Business (2019), 193–198.
31.
SharmaS.K., SharmaH. and DwivediY.K., A hybrid SEM-neural network model for predicting determinants of mobile payment services, Information Systems Management36(3) (2019), 243–261.
32.
Ajzen, The theory of planned behavior, Organizational Behavior and Human Decision Processes50(2) (1991), 179–211.
33.
Ajzen and FishbeinM., A Bayesian analysis of attribution processes, Psychological Bulletin82(2) (1975), 261.
34.
HaykinS., Adaptive radar signal processing, JohnWiley & Sons (2007).
35.
ChongA.Y.L., A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption, Expert Systems with Applications40(4) (2013), 1240–1247.
36.
SharmaS.K. and SharmaM., Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation, International Journal of Information Management44 (2019), 65–75.
37.
GiovanisA., RizomyliotisI., KonstantoulakiK. and MagrizosS., Mining the hidden seam of proximity m-payment adoption: A hybrid PLS-artificial neural network analytical approach, European Management Journal40(4) (2022), 618–631.
38.
KwonS.K. and KimS.H., A study on online word-of-mouth effect through blog reviews on fashion products-Based on the theory of planned behavior, The Research Journal of the Costume Culture21(4) (2013), 478–493.
39.
BaktiG.M.Y., RakhmawatiT., SumaediS., WidiantiT., YarmenM. and AstriniN.J., Public transport users’ WOM: An integration model of the theory of planned behavior, customer satisfaction theory, and personal norm theory, Transportation Research Procedia48(2020), 3365–3379.
40.
IsmagilovaE., SladeE.L., RanaN.P. and DwivediY.K., The effect of electronic word of mouth communications on intention to buy: A meta-analysis, Information Systems Frontiers22 (2020), 1203–1226.
41.
LadhariR., SouidenN. and DufourB., The role of emotions in utilitarian service settings: The effects of emotional satisfaction on product perception and behavioral intentions, Journal of Retailing and Consumer Services34 (2017), 10–18.
42.
TuzovicS., Frequent (flier) frustration and the dark side of word-of-web: exploring online dysfunctional behavior in online feedback forums, Journal of Services Marketing24(6) (2010), 446–457.
43.
BanduraA., Environmental Sustainabiltiy by Sociocognitive Deceleration of Population Growth, In Psychology of sustainable development. Boston, MA: Springer US. (2002), 209–238.
44.
Yazdanmehr, LiY. and WangJ., Employee responses to information security related stress: Coping and violation intention, Information Systems Journal33(3) (2023), 598–639.
45.
Ajzen, The theory of planned behavior: Frequently asked questions, Human Behavior and Emerging Technologies2(4) (2020), 314–324.
46.
ZamanianZ., OladianM. and SafariM., The relationship between self-efficacy and altruism among students of faculty of theology, Islamic Azad University, Tehran Branch, International Journal of Humanities and Cultural Studies2(3) (2015), 928–942.
47.
AlaviA., Zargham-BoroujeniA., YousefyA. and BahramiM., Altruism, the values dimension of caring self-efficacy concept in Iranian pediatric nurses, Journal of Education and Health Promotion6 (2017), 8.
48.
HuY. and KimH.J., Positive and negative eWOM motivations and hotel customers’ eWOM behavior: Does personality matter?International Journal of Hospitality Management75 (2018), 27–37.
49.
Berger and MilkmanK.L., What makes online content viral?Journal of Marketing Research49(2) (2012), 192–205.
50.
PauwS., SauterD.A., Van KleefG.A. and FischerA.H., Sense or sensibility? Social sharers’ evaluations of socio-affective vs. cognitive support in response to negative emotions, Cognition and Emotion32(6) (2018), 1247–1264.
51.
VerhagenT., NautaA. and FeldbergF., Negative online word-of-mouth: Behavioral indicator or emotional release?, Computers in Human Behavior, 29(4) (2013), 1430–1440.
52.
SparksA. and BrowningV., Complaining in cyberspace: The motives and forms of hotel guests’ complaints online, Journal of Hospitality Marketing & Management, 19(7) (2010), 797–818.
53.
PiliavinA. and CharngH.W., Altruism: A review of recent theory and research, Annual review of sociology, 16(1) (1990), 27–65.
54.
ShiauW.L. and ChauP.Y., Does altruism matter on online group buying? Perspectives from egotistic and altruistic motivation, Information Technology & People, 28(3) (2015), 677–698.
55.
YenL.A. and TangC.H.H., Hotel attribute performance, eWOM motivations, and media choice, International Journal of Hospitality Management46 (2015), 79–88.
56.
ZhangX., LiuS., DengZ. and ChenX., Knowledge sharing motivations in online health communities: A comparative study of health professionals and normal users, Computers in Human Behavior75 (2017), 797–810.
57.
Hennig-ThurauT., GwinnerK.P., WalshG. and GremlerD.D., Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet?Journal of Interactive Marketing18(1) (2004), 38–52.
58.
BronnerF. and De HoogR., Vacationers and eWOM: who posts, and why, where, and what?Journal of Travel Research50(1) (2011), 15–26.
LeeY. and KimK.H., De-motivating employees’ negative communication behaviors on anonymous social media: The role of public relations, Public Relations Review46(4) (2020), 101955.
61.
YapB., SoetartoB. and SweeneyJ.C., The relationship between electronic word-of-mouth motivations and message characteristics: The sender’s perspective, Australasian Marketing Journal21(1) (2013), 66–74.
62.
HsuH. and ChiuC.M., Predicting electronic service continuance with a decomposed theory of planned behavior, Behaviour & Information Technology23(5) (2004), 359–373.
63.
LiangT.P., HoY.T., LiY.W. and TurbanE., What drives social commerce: The role of social support and relationship quality, International Journal of Electronic Commerce16(2) (2011), 69–90.
64.
CohenS., Social relationships and health, American Psychologist59(8) (2004), 676.
65.
YangE., WangY.C. and YangJ., Hotel restaurant service employees’ sources of positive and negative emotions, Journal of Quality Assurance in Hospitality & Tourism21(5) (2020), 542–563.
66.
Meilatinova, Social commerce: Factors affecting customer repurchase and word-of-mouth intentions, International Journal of Information Management57 (2021), 102300.
67.
MittalA.K. and GargT.K., Effect of an anionic detergent—sodium dodecyl sulphate exposure on club cells in the epidermis of Clarias batrachus, Journal of Fish Biology44(5) (1994), 857–875.
68.
BoatengH. and OkoeA.F., Determinants of consumers’ attitude towards social media advertising, Journal of Creative Communications10(3) (2015), 248–258.
69.
Esmark JonesC.L., StevensJ.L., BreazealeM. and SpaidB.I., Tell it like it is: The effects of differing responses to negative online reviews, Psychology & Marketing35(12) (2018), 891–901.
70.
ChenC.J. and HungS.W., To give or to receive? Factors influencing members’ knowledge sharing and community promotion in professional virtual communities, Information & Management47(4) (2010), 226–236.
71.
Calderón GarridoC., Ferrando PieraP.J., Lorenzo SevaU., Gómez SánchezD., Fernández MontesA., Palacín LoisM. and Jiménez FonsecaP., Multidimensional Scale of Perceived Social Support (MSPSS) in cancer patients: psychometric properties and measurement invariance, Psicothema33(1) (2021), 131–138.
72.
CronbachJ., Coefficient alpha and the internal structure of tests, Psychometrika16(3) (1951), 297–334.
FornellC. and LarckerD.F., Evaluating structural equation models with unobservable variables and measurement error, Journal of Marketing Research18(1) (1981), 39–50.
75.
YangZ., TianY., FanY., LiuL., LuoY., ZhouL. and YuH., The mediating roles of caregiver social support and self-efficacy on caregiver burden in Parkinson’s disease, Journal of Affective Disorders256 (2019), 302–308.
76.
Liébana-Cabanillas, MarinkovićV. and KalinićZ., ASEM-neural network approach for predicting antecedents of m-commerceacceptance, International Journal of Information Management37(2) (2017), 14–24.
77.
LeongY., HewT.S., OoiK.B. and ChongA.Y.L., Predicting the antecedents of trust in social commerce–A hybrid structural equation modeling with neural network approach, Journal of Business Research110 (2020), 24–40.
78.
HewJ., LeongL.Y., TanG.W.H., LeeV.H. and OoiK.B., Mobile social tourism shopping: A dual-stage analysis of a multi-mediation model, Tourism Management66 (2018), 121–139.
79.
HewT.S. and Syed Abdul KadirS.L., Predicting instructional effectiveness of cloud-based virtual learning environment, Industrial Management & Data Systems116(8) (2016), 1557–1584.
80.
HaskingP.A. and OeiT.P., Alcohol expectancies, self-efficacy and coping in an alcohol-dependent sample, Addictive Behaviors32(1) (2007), 99–113.
