The effect of live streaming commerce quality on customers purchase intention extending the elaboration likelihood model with herd behaviour

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The effect of live streaming commerce quality

on customers’ purchase intention: extending the elaboration likelihood model with herd behaviour Qin Yang & Young-Chan Lee

To cite this article: Qin Yang & Young-Chan Lee (2024) The effect of live streaming

commerce quality on customers’ purchase intention: extending the elaboration likelihoodmodel with herd behaviour, Behaviour & Information Technology, 43:5, 907-928, DOI:

10.1080/0144929X.2023.2196355

To link to this article: https://doi.org/10.1080/0144929X.2023.2196355

Published online: 31 Mar 2023.

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The e ffect of live streaming commerce quality on customers’ purchase intention: extending the elaboration likelihood model with herd behaviour

Qin Yang aand Young-Chan Lee b

a

School of Economics, Jiaxing University, Jiaxing, People ’s Republic of China; b

Department of Business Administration, College of Management and Economics, Dongguk University, Gyeongju, Korea

ABSTRACT

This study examines how technology quality, experience quality, and herd behaviour in live

streaming commerce a ffect customers’ purchase intention We proposed an integrated research

model based on the elaboration likelihood model (ELM) and herd behaviour This study used

covariance-based structural equation modelling (CB-SEM) to analyse data and assess the

research model and hypotheses We surveyed 872 Chinese customers who have experience in

live streaming commerce, from which the data of 845 were used to test the hypotheses Our

findings show that good technology and experience quality lead customers to discount their

own information and imitate their peers Customers ’ herd behaviour positively affects their

purchase intention Further, discounting own information positively mediates the indirect link

between live streaming commerce quality (technology quality and experience quality),

imitation, and customers ’ purchase intention This study is the first to combine live streaming

commerce quality and herd behaviour to investigate customers ’ purchase intention in live

streaming commerce It highlights the value of incorporating herd behaviour into the ELM and

adds to the body of knowledge by providing a deeper insight into customers ’ purchase

intention in live streaming shopping It also has managerial implications for live streaming

commerce practitioners to sever the sustainable growth of e-commerce.

ARTICLE HISTORY

Received 10 July 2022 Accepted 25 January 2023

KEYWORDS

Live streaming commerce; technology quality; experience quality; elaboration likelihood model; herd behaviour; customers ’ purchase intention

1 Introduction

Live streaming commerce is a business model in

which a streamer sells goods and services directly

to customers via online video streams in which the

former exhibits, discusses, and responds to audience

inquiries in real time A live streaming session may

occur on an e-commerce website or social media

platform It can be store-specific or brand-specific,

and the streamer can also hold live streaming events

where they promote products from various suppliers

Live streaming commerce combines instant product

purchases with customer interaction via chatting or

reaction buttons In the last few years, live streaming

commerce has greatly impacted the retail industry in

China and has become a major sales channel Many

traditional e-commerce vendors are transforming

their business and products on famous live

stream-ing platforms such as Taobao Live, Douyin,

Kuaishou, etc According to research data from

China Internet Watch, the gross merchandise value

(GMV) of e-commerce live streaming is estimated

to account for over 20% of China’s online shopping

GMV by 2022 (Source: www.forbes.com; www.statista.com)

Research data from Statista.com and na.com shows that in 2020, e-commerce live streaming

iResearchChi-in ChiResearchChi-ina represented 10.6% of the total onliResearchChi-ine shoppiResearchChi-inggross merchandise value (GMV) The proportion wasestimated to grow to 24.3% in 2023 According to a

2022 survey of Chinese consumers, around 43% of therespondents aged between 25 and 30 confirmed thatthey watched shopping-related live streaming The sur-vey also showed that 39% of all age groups of respon-dents used live commerce Live streaming is vital forbrands to increase sales and for small-scale operatorssuch as farmers to reach consumers more effectively

It grew exponentially during the pandemic, encouragingpeople to shop online and gain interactive and immer-sive experiences amid lockdowns The e-commercegiant Alibaba’s Taobao Live has captured the lion’sshare of live streaming consumers, accounting for68.5%, followed by Douyin and Kuaishou Othermajor Chinese internet players like JD.com and Baiduare also trying to grow their presence in the market

CONTACT Young-Chan Lee chanlee@dongguk.ac.kr Department of Business Administration, College of Management and Economics, Dongguk University, 38066, Gyeongju, Korea

2024, VOL 43, NO 5, 907 –928

https://doi.org/10.1080/0144929X.2023.2196355

In addition, the‘2022 China Retail Digitalization White

Paper,’ jointly published by McKinsey and the China

Chain Store & Franchise Association (CCFA), suggests

that the onset of COVID-19 has accelerated the

adop-tion of new retail technologies and that online channels

have flourished and fragmented Retailers need to not

only manage online communities (e.g Weibo or

Wechat official accounts) but also create strategies for

social platforms(e.g Xiaohongshu), and short video

live streaming platforms(e.g Douyin)

Compared to traditional e-commerce, live

stream-ing commerce offers significant benefits and helps

cus-tomers make more informed purchasing decisions On

a live streaming commerce platform, a streamer

com-municates precise and comprehensive information

about products by combining real-time visual

rep-resentations of the products with their own

move-ments (Gao et al 2021) Further, customers can

assess the streamer’s trustworthiness and attractiveness

by examining simple clues or instructive inferences in

the live streaming environment based on the

strea-mer’s appearance, tailored services, and guidance

(Wongkitrungrueng and Assarut 2020) Customers

can also ask questions spontaneously on the bullet

screen and receive live responses from the streamer,

increasing the currency of the information available

to them (Gao et al.2021) From observing their fellow

co-viewers, customers can acquire valuable knowledge

and use the bullet-screen remarks and responses of

others to make more informed purchasing decisions

(Wongkitrungrueng and Assarut 2020; Yu et al

2018) In sum, live streaming commerce allows

custo-mers to better evaluate products and make educated

consumption decisions, as it provides them with

infor-mation signals and persuasive messages (Zhou, Chen,

and Su 2019; Chen, Zhao, and Wang 2020) It is

notable that products on live streaming commerce

platforms have a much higher purchase rate than

those on traditional e-commerce platforms (Lu and

Chen2021)

Past studies have explored customer behaviour in

live streaming shopping from various aspects such as

continuance intention (Zhang et al 2022), stickiness

(Li, Li, and Cai 2021), purchase intention (Gao et al

2021; Lu and Chen 2021; Meng et al 2021; Doong

2021; Park and Lin 2020; Zhang et al 2020; Sun

et al.2019), response intention (Gao et al 2021),

cus-tomer engagement behaviour (Kang et al 2021; Guo

et al 2021; Wongkitrungrueng and Assarut 2020),

and urge to buy impulsively (Zuo and Xiao 2021)

Those studies have found a number of factors that

explain why people use live streaming shopping and

buy products from live streaming shopping The

critical factors include trust-related variables (e.g.trust in streamers or products), psychology-relatedvariables (e.g attachment to streamers or platforms,psychological distance, etc.), and relationship variables(e.g swift guanxi, tie strength, etc.) However, onecrucial phenomenon (i.e herd behaviour) has beenoverlooked Herd behaviour is frequently a beneficialtool in marketing and, when employed correctly, canresult in increased sales and changes to the socialstructure It has been identified as particularly influen-tial for user behaviour in various information systems(IS) fields For example, Shen, Zhang, and Zhao(2016) extended past research by incorporating theinfluence of herd behaviour to investigate the adop-tion of online reviews in online communities Theirfindings showed that herd factors positively influenceduser information adoption Vedadi, Warkentin, andDennis (2021) identified that herd factors substantiallyaffected information security decisions Herd behav-iour was also discussed in the social media area,Mattke et al (2020) analysed click-through andview-through intention based on herd theory In e-commerce, research on herd behaviour is still veryrare One recent study by Erjavec and Manfreda(2022) identified herd behaviour as a critical determi-nant of online shopping adoption After a comprehen-sive review of the previous literature, to the best of theauthors’ knowledge, a few studies investigated theherd behaviour behind customers’ purchase intention

in the context of live streaming shopping As a result,this study aims to fill in the gaps in the research onlive streaming shopping

Furthermore, previous research in the field of livestreaming commerce primarily used four frameworks:

1 Technical characteristics-customer behaviour work, a framework that focuses on the influences of alive streaming commerce platform’s technology charac-teristics such as visibility, metavoicing, and guidanceshopping affordance (Sun et al.2019) 2 Social charac-teristics-customer behaviour framework, a frameworkthat emphasises the social stimulus generated from theexternal environment, such as responsiveness and per-sonalisation (Kang et al 2021) 3 Technical & socialcharacteristics-customer behaviour framework, thecooperation between the technology and interpersonalsubsystem (Li, Li, and Cai 2021; Zhang et al 2022)

4 Streamer characteristics-customer behaviour work, a framework that integrates the characteristics

frame-of the streamer (e.g streamer trustworthiness and mer attractiveness) and customers’ behavioural inten-tions (Zuo and Xiao 2021; Park and Lin 2020) Forinstance, Li, Li, and Cai (2021) explored the effects ofsocial system factors and technical system factors on

strea-customers’ attachment to streamers and platforms and

mediating influence of users’ attachment on stickiness

Gao et al (2021) analysed how the viewers processed

the persuasive message in live streaming commerce

from the perspectives of information quality, streamer

quality, and system quality Zhang et al (2022)

exam-ined customers’ intention to continue using live

stream-ing commerce from the point of view of social enablers

and technical enablers Additionally, Guo et al (2021)

and Wongkitrungrueng and Assarut (2020) also

investi-gated the relationship between trust and consumer

engagement From a social point of view, Kang et al

(2021) looked into how customer engagement changes

over time based on the strength of ties in live streaming

commerce However, an integrated framework that

examines key technological (i.e technology quality),

experiential (i.e experience quality), and herd

behav-ioural (i.e discounting own information and imitation)

determinants of purchase intention in live streaming

commerce is lacking Therefore, it is necessary to fill

the existing gaps by examining technological,

experien-tial, and herding factors to understand how and when

purchase intention emerges in live streaming shopping

Live streaming commerce represents a connection not

only to information technology but also to streamers

and peers Therefore, there is a need to analyse the

role of technology quality, experience quality, and

herd behaviour in the live streaming shopping context

to provide researchers and practitioners with a full

understanding of how customers develop purchase

intention and their border requirements The current

literature demonstrates a lack of research in this area,

especially concerning the Chinese live streaming

indus-try context

The findings of this study add to the existing body

of knowledge in several aspects First, we applied the

ELM to investigate the quality-related factors that

led customers to form herd behaviours This

broad-ened the scope of the study beyond the social

facilita-tors and technological enablers that had been

discussed in previous research Second, this study

extended the ELM to include herd behaviour in a

novel research context, demonstrating that herd

behaviour influences purchase intention Third, we

assessed quality-related factors (technology and

experience quality) as second-order constructs, which

provided insights into how to interpret customer

behaviour on a quality basis Finally, the findings aid

live streaming commerce vendors in cultivating their

customers’ herd behaviour, which is critical for

devel-oping purchase intention, and provide valuable

impli-cations for practitioners developing sustainable live

streaming commerce marketing strategies

2 Theoretical background and hypothesisdevelopment

2.1 Live streaming commerce

Live streaming commerce offers the convenience of time shopping, gives customers comprehensive infor-mation from multiple perspectives, and allows them tomake informed choices (Cai et al 2018; Wongkitrun-grueng, Dehouche, and Assarut2020) It has receivedadequate academic attention in the last few yearsbecause of its growing popularity (seeTable 1) Manyrecent studies investigated customers’ behaviour inten-tions in live streaming shopping scenarios, such as pur-chase intention, continuance intention, and responseintention The potential influencing factors includetechnology-enabled functions or mechanisms, such asinteractivity (Kang et al 2021), information quality(Gao et al 2021), personalisation, visibility (Zhang

real-et al.2022; Sun et al.2019), metavoicing, and guidanceshopping (Sun et al.2019) Product-related factors arealso found to be crucial determinants of behaviouralintentions, such as product popularity (Kang et al

2021), product fit uncertainty, product quality tainty (Guo et al.2021), and perceived product quality(Chen, Zhao, and Wang2020) Mistrusted live stream-ing platforms or streamers exist since China’s livestreaming commerce market is still in the developmentstage, which is one of the critical reasons customers arereluctant to buy products from live streaming shopping.Several scholars mentioned this issues and discussed thefactors that influence customer trust Chen, Zhao, andWang (2020) examined the mechanism behind consu-mers’ trust building and purchasing intention in livestreaming commerce They also discovered that in livestreaming commerce, there is a trust transfer effectfrom streamer to product Similarly, Zhang et al.(2022) investigated the influence of social and techno-logical enablers on trust and how trust influences consu-mers’ intention to continue using a service in the context

uncer-of live streaming commerce Wongkitrungrueng andAssarut (2020) developed a comprehensive frameworkand examined the relationships between customers’ per-ceived value of live streaming and customer trust Theirfindings showed that trust in products positively affectedtrust in sellers, which provided additional insights intothe phenomena that drive customers to participate inlive streaming commerce In addition, because livestreaming shopping platforms offer social networkingcapabilities, social factors like tie strength and swiftguanxi were found to positively influence customers’purchase intentions and engagement in live streamingcommerce (Kang et al 2021; Guo et al 2021; Chen,Tsai, and Tang2021)

In summary, most of the research on customer

behaviour in live streaming commerce reveals a

ten-dency to examine technological or social factors

How-ever, live streaming commerce provides a

technological platform for customers to buy products

and enhances customers’ shopping experience Studies

examining key technological, experiential, and herding

determinants of customers’ purchase intention are

lack-ing This study aims to analyse the quality of live

streaming commerce from technical aspects, such as

vicarious expression and visibility, and experiential

elements, like immersion, emotion, symbolic value,

and admiration Additionally, it will investigate the

impact of customers’ herd behaviour, such as

discount-ing personal information and imitation, on their

pur-chase intentions in live streaming commerce

2.2 Elaboration likelihood model (ELM)

The ELM is based on a dual-process theory and explains

how persuasion influences individuals’ attitudes in two

ways: using central and peripheral information

proces-sing Before making a decision, the central route

involves contemplating messages and examining the

quality of their arguments, such as evaluating their

rela-tive merits and relevance (Bhattacherjee and Sanford

2006) As customers take a peripheral route, they

spend less effort judging information related to products

and pay more attention to the opinions of other people,such as comments left by their previous customers andthe star rating of the products (Filieri and McLeay2014;Shihab and Putri2019; Tang, Jang, and Morrison2012).Throughout the literature, the ELM has often been used

to explain persuasive information behaviour; therefore,this framework provides a strong theoretical andempirical basis for studying behaviour in live streamingcommerce This way, the central and peripheral routeswould be demonstrated to have a significant influence

on the customers’ herd behaviour to purchase fromlive streaming commerce, and this herd behaviourwould further drive them to make a purchase decision.The ELM has been used extensively to explainchanges in attitudes toward, or perceptions of, specifictopics in a wide range of disciplines, including market-ing, psychology, health, education, tourism, humanresources, information technology, environment, andentrepreneurship In the field of information systems(IS), the ELM has been looked at from several perspec-tives In Meng and Choi (2019), the ELM concept isintegrated into the theory of planned behaviour toinvestigate tourists’ intentions to use location-based ser-vices Using a combination of the ELM, commitment–trust theory, and social presence theory, Bao andWang (2021) identified the mechanisms of consumerparticipation in brand microblogs Based on the ELM,Shi et al (2018) proposed a theoretical framework for

Table 1.Review of the most recent studies on live streaming commerce

Dependent variables Zhang et al ( 2022 ) Social enablers, Technical enablers Trust in streamers, Trust in

products

Live streaming genre

Continuance intention

Li, Li, and Cai ( 2021 ) Social system factors, Technical system factors Attachment to streamers and

platform

Gao et al ( 2021 ) Information completeness, Information accuracy,

Information currency, Streamer trustworthiness, Streamer attractiveness, Bullet-screen consistency, Co-view involvement

Perceived persuasiveness Mindfulness Purchase intention,

Response intention Kang et al ( 2021 ) Responsiveness, Personalization Tie strength Popularity,

Tenure of membership

Customer engagement behaviour

engagement Zuo and Xiao ( 2021 ) Streamer attractiveness, Social presence,

Doong ( 2021 ) Reputation, Interactivity Trust, Perceived value n/a Purchase intention Park and Lin ( 2020 ) Wanghong-product fit, Live content-product fit Wanghong trustworthiness,

Wanghong attractiveness, Utilitarian attitude, Hedonic attitude

n/a Purchase intention

Wongkitrungrueng and

Assarut ( 2020 )

Utilitarian value, Hedonic value, Symbolic value Trust in product, Trust in seller n/a Customer

engagement Zhang et al ( 2020 ) Live video streaming strategy Psychological distance, Perceived

uncertainty

Product type Online purchase

intention Sun et al ( 2019 ) Visibility, Metavoicing, Guidance shopping Immersion, Presence n/a Purchase intention

the systematic investigation of the factors determining

individual dissemination behaviours on social

network-ing services (SNSs) Zhou (2021) examined users’

inten-tion to adopt informainten-tion in online health communities

using the ELM Chen, Tsai, and Tang (2021)

investi-gated the influence of informational-based readiness,

customer readiness, and social influence on the

inten-tion to utilise self-service stores and the moderating

effects of social influence The ELM model has also

been applied in several studies in the live streaming

commercefield For example, using the ELM and trust

transfer theory, Chen, Zhao, and Wang (2020) explored

how live streaming affects consumers’ trust and

pur-chase intention By integrating mindfulness as a

mod-erator, Gao et al (2021) constructed an ELM to assess

the influences of central and peripheral factors on

per-ceived persuasiveness in live streaming commerce

Nonetheless, there has been little analysis of customers’

purchase intention in live streaming commerce

inte-grating the ELM and herd behaviour Therefore, this

study examines customers’ herd behaviour and

pur-chase intention by considering technology

quality-related factors as central route factors and experience

quality-related factors as peripheral route factors

2.2.1 Central route factors

The live streaming commerce platform comprises

exter-nal technological characteristics and features as part of

its status as a unique type of information system (IS)

As part of the current study, a measurable technology

quality of live streaming commerce is proposed as the

combination of the technology characteristics of the

live streaming commerce platform The technological

system’s processes, tasks, and technologies are essential

in transforming inputs into outputs (Li, Li, and Cai

2021; Bostrom and Heinen1977) Vicarious expression

and visibility are typical representatives of the central

route factors as they reveal the quality of technology

embedded in the live streaming commerce platform

With the development of collaborative properties of

social media, advanced technology can facilitate live

streaming commerce with excellent customer

relation-ship capabilities Customers can communicate with

ser-vice providers efficiently and conveniently using the

intermediary technological device

In recent years, some studies have examined the

tech-nological characteristics of live streaming commerce

For example, Zhang et al (2022) developed a theoretical

model to examine how social and technological enablers

might influence the development of trust and whether

trust affects customers’ continuance intentions

Simi-larly, Li, Li, and Cai (2021) proposed a

socio-technologi-cal model to explain how live streaming services affect

user stickiness Platform-based services allow customers

to participate in exchange-related activities and ences Using conceptual frameworks derived from ISusage and service marketing literature, this study exam-ines the technology quality of live streaming commercefocusing on vicarious expression and visibility Vicar-ious expression refers to the display of a streamer’s con-sumption of products to enable viewers to experiencewhat it is like to consume these products (Yi andDavis 2003; Li, Li, and Cai 2021) Further, visibilityrefers to the ability to present products visually to theuser, which is crucial to the interaction between custo-mers and streamers in live streaming shopping (Zhang

experi-et al.2022)

Chen et al (2019) identified four functions of ious expression: analogy mapping, trialability, vicariousexperience demonstrability, and transferring First, ana-logous mapping describes some characteristics that aresimilar between a viewer and a streamer, which canenrich viewers’ experiences (Yi and Davis2003; Li, Li,and Cai2021) Live streaming can trigger different feel-ings in individuals (Huang, Lurie, and Mitra2009) Forinstance, the same coat displayed by streamers mayevoke different emotions in different customers.Through analogy, viewers can see the streamer as a sub-stitute for themselves, thus solving the problem of indi-vidual differences This can effectively compensate forthe lack of an individual experience in traditional onlineshopping Second, trialability refers to the streamers’ability to test products in response to viewer requests(Moore and Benbasat1991) The third function is vicar-ious experience demonstrability, which means a strea-mer can demonstrate the products’ physicalappearance and express their psychological impressions

vicar-of products Customers can get to know the productinformation better with the help of a streamer by show-ing the products based on their needs (Li, Li, and Cai

2021) Lastly, transferring entails a streamer taking onthe customer’s role For example, streamers can beasked to perform certain actions (e.g trying on acoat) Viewers can imagine what it would be like touse a product through streamers’ displays

Besides, visibility is another critical technological tor of live streaming commerce Traditionally, e-com-merce makes sellers invisible to their customers(Treem and Leonardi2013), making it difficult for thecustomers to develop trust relationships and acquireclues to interpersonal communication (Bai, Yao, andDou2015; Zhang et al.2022) By communicating visu-ally in live streaming, customers can see streamers’ truereflection, reducing their perceived distance (Lv, Jin,and Huang 2018) and enabling social connection(O’Riordan, Feller, and Nagle2016)

fac-2.2.2 Peripheral route factors

In addition to central routes, customers are also

influenced by peripheral cues in processing shopping

information and developing their evaluation of

pro-ducts This study proposed experienced quality as the

peripheral route of live streaming shopping, which

cus-tomers use to assess product recommendations The

experience quality conception considers how customers

react to the psychological benefits they expect from live

streaming shopping (Hu, Dai, and Salam 2019)

Research on consumer behaviour has become

increas-ingly focused on customers’ experience (Novak,

Hoffman, and Yung 2000; Rose et al 2012; Kumar

and Anjaly 2017) The technical-experiential approach

of this study provides a better and deeper understanding

of how live streaming commerce affects customers’

pur-chasing intentions Watching and participating in live

streaming commerce can be emotional and experiential

in nature (Hu, Dai, and Salam2019) With live

stream-ing commerce, customers can connect and share

infor-mation with streamers and other customers As a result,

customers develop emotions and sensations that are

live, exciting, and simultaneous, and feel fully

immersed The creation and delivery of services are

dri-ven by the customers’ experiences (Heinonen and

Strandvik 2015; Strandvik, Holmlund, and Edvardsson

2012) Accordingly, experience quality is proposed in

this study as customers’ experiential feelings, emotions,

and sensations associated with live streaming

com-merce Based on a literature review of service marketing

and IS usage, this study defines experience quality as a

multi-dimensional construct, identifying four

dimen-sions: immersion, emotion, symbolic value, and

admiration

Live streaming shopping immersion is defined as

the customers’ feeling when they become engrossed

in, involved with, and absorbed by their experience

(Yim, Chu, and Sauer 2017) Customers may be able

to identify the values and benefits they can obtain

from an activity based on their experience with

per-ceived immersion (Fang et al.2018) Emotion describes

consumer experiences associated with exchange-based

activities in live streaming commerce (Hu, Dai, and

Salam 2019) An emotional connection occurs when

a viewer is positioned in a fast-paced, interactive chat

environment, and those emotions are subsequently

expressed in their responses to the live-streamer or

others in the chat (Lim et al 2015) An important

source of competitive advantage is the customer

experience (Schmitt 1999) The customer’s individual

evaluation of an experience results in the creation of

an individual experience Experience is characterised

by ‘uniqueness that sets an activity apart from others’

(Palmer 2010) Moreover, customer experiences thatare memorable, unique, and sustainable over time aresuccessful (Pine and Gilmore 1998) When someoneshops via live streaming, he/she creates symbolicmeanings, social codes, relationships, and identitiesabout him/herself (Firat and Venkatesh 1993) Thus,for customers, an experience that can enhance theiridentity and reflect their personality is important(Wongkitrungrueng and Assarut 2020) Furthermore,

as an aspect of positive psychology, admiration refers

to high levels of esteem or praise for notable uals or role models and occurs when people observesomeone with exceptional abilities or virtuous behav-iour (Becker and Luthar 2007; Immordino-Yang

we aim to explore how consumers’ perception of nology quality affects their admiration The audience’sadmiration for the streamer will be expressed throughbullets on the screen after watching live streaming withvivid product demonstration and professional expla-nations Based on the above discussion, we proposethe following hypothesis:

tech-H1: In live streaming commerce, technology quality ispositively associated with experience quality

2.4 Herd behaviour

There is considerable discussion regarding herd

behav-iour in various fields, including finance, consumer

behaviour, and organisational decision-making (Erjavec

and Manfreda2022) An example of this would be when

decision-makers use information about what other

people do even though their private data suggest

doing something quite different (Banerjee1992)

There-fore, herd behaviour can be described as a form of

heur-istic where individuals conform to the preferences of the

majority in their environment by choosing similar

beha-viours (Çelen and Kariv2004; Antony and Joseph2017)

Banerjee (1992) stated that herd behaviour is

character-ised by two factors, namely discounting own

infor-mation and imitation Discounting their own

information refers to the extent to which people do

not consider their information and beliefs when making

decisions The imitation of herding behaviours involves

individuals following other people’s decisions

Research on herd behaviour focuses mostly on

dis-crete decisions across different disciplines, such as

decisions on online reviews adoption (Shen, Zhang,

and Zhao 2016), online shopping adoption (Erjavec

and Manfreda 2022), information security (Vedadi,

Warkentin, and Dennis2021), social media herd

behav-iour (Mattke et al 2020), and information security

message persuasiveness (Xu and Warkentin2020)

Cus-tomers can purchase based on their assessment of the

situation in live streaming commerce It often involves

searching for information and evaluating the results

The nature of live streaming commerce is relatively

complex; therefore, a detailed assessment requires

specific knowledge and a considerable investment of

time and energy (Duan, Gu, and Whinston2009;

Jasper-son, Carter, and Zmud 2005) A lack of information

regarding the live streaming business can raise

uncer-tainty over its appropriateness (Sun2013)

It is more effective to follow the herd than to invest

one’s own time and efforts This approach assumes

that the herd has completed the investigation and

assessment process and determined that purchasing

from a live streaming commerce platform is reasonable

While watching live streaming, customers are more

likely to trust the streamer and other customers as

they perceive good technology quality and have a

plea-sant experience They tend to disregard their beliefs and

copy other customers’ actions (Sun2013) The customer

ultimately incorporates other people’s behaviour into

their preconceived notions and makes a herd-based

decision based on group behaviour (Vedadi, Warkentin,

and Dennis2021) In summary, the fact that many

cus-tomers purchase from live streaming commerce may

signify its popularity Thus, the streamer’s performanceand the number of current customers in live streamingcommerce may send signals, such as purchasing signals,that influence a customer to purchase from live stream-ing commerce

2.5 The effect of live streaming commercequality on discounting own information

Discounting own information in live streaming merce involves the degree to which a customer disre-gards their own information when making a purchasedecision and consults with other customers instead.Different customers feel differently when watching livestreaming shopping (Huang, Lurie, and Mitra 2009).Vicarious expression enables customers to see theanchor as a substitute for themselves, and this solvesthe problem of individual differences This, in turn,can effectively compensate for the lack of personalisa-tion in traditional e-commerce By enabling visibility,customers can see the streamers in live shopping, creat-ing psychological closeness and fostering social connec-tions (Lv, Jin, and Huang 2018) Thus, live streamingleads to a deeper relationship between customers and

com-a grecom-ater level of trust in strecom-amers com-as they spendmore time watching it According to prior research onherding, it can be viewed as a form of social inter-action-based behaviour (Chen, Wang, and Xie 2011).Herding describes how individuals learn from and fol-low other persons’ behaviours Live streaming shoppingwith high technology quality enables streamers, custo-mers, and other peers to communicate and exchangeideas efficiently In light of this, we anticipate a strongrelationship between technology quality and discount-ing our own information Therefore, this study postu-lates the following hypothesis:

H2: In live streaming commerce, technology quality ispositively associated with discounting owninformation

In addition to advanced technological features, livestreaming shopping promises a rich user experience aswell An emotional connection is an assessment of anindividual’s perceptions and judgments about thecharacteristics of their partners (Hansen, Morrow, andBatista2002) and associates Through emotional bond-ing, this study forms an emotional association betweenthe streamer, customers, and peers (Kim and Park

2013) Live streaming commerce can enhance the mer’s shopping experience and make it more exciting byenhancing their hedonic experience These positiveexperiences can help establish an emotional connectionamong the streamer, customers, and other peers

custo-Customers who obtain a pleasant emotional state while

watching live streaming shopping will be more inclined

to participate actively in shopping-related activities and

exhibit a more positive attitude toward the products

presented In this case, customers are more likely to

trust the streamer and other peers when they have a

positive mental state and experience, thus relying on

the herd and being less attentive to their own

information

While watching live streaming, customers have

per-ceptions of themselves, the streamer, and other

custo-mers (Wongkitrungrueng and Assarut 2020) People

usually tend to shop at places where they can associate

with others like themselves In live streaming shopping,

streamers and viewers share the same platform

Conse-quently, social identification—a self-defining process

that indicates belongingness to a group of people

(Ash-forth and Mael1989) and establishes self-efficacy

(Bhat-tacharya and Sen 2003)—can occur in relation to an

individual (streamer) and a larger group (other

custo-mers) (Vukadin, Wongkitrungrueng, and Assarut

2018) In this regard, customers will tend to trust the

streamer and other customers with the same

identifi-cation They may be more inclined to rely on

infor-mation recommended by others than their own

information Moreover, the admiration for streamers

also positively impacts customers’ attitudes,

partici-pation, and loyalty (You and Robert2018; Trivedi and

Sama 2020) Additionally, customers watching the live

streaming will unconsciously show their admiration

emotion via the screen bullets; this causes them to

res-onate emotionally and further increases their

admira-tion Hence, admiration emotion can contribute to the

psychological bond between the streamer and viewers

(Meng et al.2021) Customers are more likely to build

positive relationships with the streamer they admire

and have positive attitudes toward the products the

streamer recommends Therefore, they are less likely

to rely on their own information but trust the streamer

instead Hence, the following hypothesis is proposed:

H3: In live streaming commerce, experience quality

is positively associated with discounting own

information

2.6 The effect of discounting own information on

imitation and purchase intention

A customer’s imitation in a live streaming shopping

scenario is defined as the measure of how much the

cus-tomer is influenced by other customers’ choices or

decisions while watching Customers who discount

their own information are more likely to imitate the

actions of others rather than make a decision entirelybased on their own information and assessment.When a consumer has dismissed their own thoughts,

it is appropriate for the customer to replicate otherpeople’s actions in similar circumstances (Au andKauffman 2003; Thies, Wessel, and Benlian 2016).Therefore, we argue that when customers discounttheir own information, imitation becomes an acceptablealternative strategy, as customers might conclude thatthe streamer and other peers provide better and morecomplete information to them on everything related tolive streaming shopping Hence, we propose the follow-ing hypothesis:

H4: In live streaming commerce, discounting owninformation is positively associated with customers’tendency to imitate the behavior of others

Additionally, we propose that customers’ decision

to purchase products and accept services from livestreaming commerce is positively associated withherd factors This is consistent with the notion thatherding may have an effect on how individuals makedecisions (Duan, Gu, and Whinston 2009) Accord-ingly, we suggest that discounting one’s informationmay positively affect purchase intention in live stream-ing commerce When customers discount their owninformation, they need to seek external information

to increase the likelihood of making the right choice(Chen and Chaiken 1999) When it comes to livestreaming commerce, this means that customers arelikely to refer to and even rely on online recommen-dations from other audiences (Zhang et al 2014) As

a result, external information’s influence may outweightheir own evaluation’s influence in this scenario Inturn, consumers will be more inclined to accept sug-gestions from other audiences and become intend tobuy from live streaming commerce Thus, we proposethe following hypothesis

H5: In live streaming commerce, discounting owninformation is positively associated with customers’purchase intention

2.7 The effect of imitation on purchase intention

Banerjee (1992) described a logical strategy for imitation

as‘adopting the behavior of others.’ In the context ofthis study, it means choosing to acquire things based

on the suggestions of other customers on a live ing commerce platform Usually, to avoid being per-ceived as incompetent if investments generate lowreturns, somefinance investors replicate the investmentstrategies of professional investment managers (Scharf-stein and Stein1990) A person might prefer the chances

stream-of being mistaken with everyone else over the risk stream-of

making an uncommon prediction that turns out to be

the only correct one (Graham1999) and may consider

imitation to be an effective approach for dealing with

uncertainty (Field et al 2006) Similarly, a variety of

informational cues, including online reviews and

evalu-ations, are frequently provided in live streaming

shop-ping context The use of these cues may have an

influence on herd behaviour and lead to a strong

ten-dency to imitate others’ behaviours Customers who

have a strong proclivity to imitate others’ actions

nor-mally use online informational cues to determine the

quality and popularity of products (Park, Lee, and

Han 2007) By evaluating how other audiences have

assessed and reviewed products, customers can learn

about and follow the purchasing decisions of others

As Zhang and Liu (2012) suggested, people tend to

jus-tify their imitating behaviour by learning deliberately

from others Research on the adoption of an IS also

pro-vides empirical evidence of the relationship between

imitation and behavioural intention (Sun 2013) In

light of these considerations, we suggest that imitation

may influence customers’ purchase intention in live

streaming commerce Consequently, we propose the

fol-lowing hypothesis:

H6: In live streaming commerce, imitation is positively

associated with customers’ purchase intention

3 Methodology and research design

3.1 Measurement development

We have nine variables in the questionnaire, namely,

vicarious expression (VE), visibility (VIS), immersion

(IMME), emotion (EMO), symbolic value (SV),

admira-tion (ADM), discounting own informaadmira-tion (DOI),

imi-tation (IMI), and purchase intention (PI) All

measurement items were developed from the

estab-lished scale and revised appropriately according to the

current study context We adopted measurement

items of vicarious expression and visibility for

technol-ogy quality from the studies of Li, Li, and Cai (2021) and

Zhang et al (2022) Further, we developed the

measure-ment items of immersion, emotion, symbolic value, and

admiration for experience quality by reviewing the

works of Hu, Dai, and Salam (2019), Wongkitrungrueng

and Assarut (2020), and Meng et al (2021) The items

assessed herd behaviour (i.e discounting own

infor-mation and imitation) by referring to the works of

Erja-vec and Manfreda (2022) and Mattke et al (2020)

Lastly, we measured and developed the items of

custo-mers’ purchase intention according to the study of

Gao et al (2021) A Likert five-point scale (from 1 =

strongly disagree to 5 = strongly agree) was applied tomeasure these items

To verify these hypotheses, we conducted a survey toevaluate our research model As we collected the surveydata in China, wefirst translated the original items intoChinese and then adopted the back-translation toensure the accuracy of the items’ intended meaningbetween English and Chinese Moreover, to ensure thesurvey’s validity, we invited two professors in the e-commerce major and two live streaming commerceplatform managers to review the questionnaire Afterthe review, one item measuring admiration and oneitem measuring emotion were removed The validmeasurement items are presented in Appendix

A Then, the formal questionnaires were distributed tothe potential respondents

3.2 Sampling and data collection

Customers who have had live streaming shoppingexperiences are the target respondents of this study,regardless of the categories of the products In 2022,the total revenue of China’s e-commerce live streamingsector is projected to reach RMB 1.2 trillion (US$180billion) with a total of 660 million viewers Thisfigure

is expected to grow to RMB 4.9 trillion (US$720 billion)

in 2023, according to a 2021 iResearch report This willaccount for 11.7 percent of total e-commerce sales in thecountry, injecting new impetus into the economy.Because the live streaming commerce market in China

is rapidly developing and China has a large population,this study focused on investigating Chinese customersand decided to collect data in China

The survey was conducted via a famous and reliableonline service, Wenjuanxing (www.wjx.cn) To main-tain the quality and precision of the questionnaires,

we used the premium service provided by the surveyprovider In the questionnaire, wefirst ask the respon-dents whether they have experience shopping on livestreaming commerce platforms If their answer is‘yes,’they can continue the survey; however, if their answer

is ‘no,’ the survey will stop automatically Randomsampling was applied for this study to target all thepotential live streaming commerce experienced custo-mers The surveying period lasted from early February

to late March 2022 Overall, 872 participants took thesurvey After collecting the data, we checked the surveysources and found that our collected questionnairescovered most of the provinces of China To keep the val-idity of the questionnaires, we checked the IP addressand time spent by each respondent in answering thesurvey Finally, 845 valid questionnaires remained.The effective recovery rate was 96.9% The sample size

for the data was determined based on the guidelines

pre-sented by Ahmed et al (2021), which state that a sample

size of 50 is regarded as inadequate, 300 as good, 500 as

very good, and 1000 as an excellent sample for factor

analysis Therefore, the sample size of this study is

sat-isfactory for performing estimations

As shown inTable 2, 381 are male (45.1%) among all

valid samples, and 464 are female (54.9%) Most of the

respondents are aged between 20 and 29 years (n =

345, 40.8%), and most of them have an undergraduate

education background (n = 437, 51.7%) As to their

occupations, 198 of them are students (23.4%), 174 are

self-employed (20.6%), and 338 are working in private

or state-owned companies (40%) Further, 498

respon-dents have a monthly income between 3000–9000

RMB (59%) As for the use period of live streaming

shopping, most have used it for more than one year

(n = 502, 59.4%) Detailed information on demographic

characteristics can be found inTable 2

3.3 Preliminary analysis

Podsakoff and Organ (1986) suggested that common

method variance may exist in single-source data We

performed Harman’s single-factor test to check

com-mon method bias (CMB) in the collected data All the

measurement items were loaded into a principal ponent without rotation It is suggested that the pro-blem with CMB exists if the total variance of onefactor exceeds 50% The first factor accounts for33.14% of this study’s variance (less than 50%) Thus,the data do not have a problem with CMB

com-In addition to CMB, we checked the normality of thedistributions of all the major research variables Hair

et al (2010) and Byrne (2013) argued that data is sidered to be normal if skewness is between−2 to +2and kurtosis is between−7 to +7 The values of skew-ness in this study range from −1.436 to −1.026, andthe values of kurtosis range from 1.206 to 3.12, indicat-ing an acceptable level Therefore, we continue to adoptthe variables in the following analyses A multicollinear-ity test is the next step to determine whether there aresimilarities between the variables in the proposedmodel Multicollinearity refers to a situation in whichtwo or more predictor variables strongly correlate and

con-do not contribute distinct information to the regressionmodel When the degree of correlation between vari-ables is sufficiently strong, it can create difficulties inboth the fitting process and the interpretation of themodel One method for detecting multicollinearity is

to use the variance inflation factor (VIF), a statisticthat evaluates the correlation between predictor vari-ables in a regression model By reviewing the works ofLiang et al (2012) and Leong, Jaafar, and Ainin(2018), the existence of multicollinearity was evaluatedvia the VIF and collinearity tolerance Hair et al.(2016) and Gao et al (2021) suggested that when theVIF was below 10, and the tolerance was above 0.10,this indicated that there was no multicollinearity pro-blem The VIFs of the items measured in the currentstudy range from 1.376 to 1.726, which means multicol-linearity is unlikely to be a problem

4 Data analysis and resultsConsidering the research model, research purpose, anddata characteristics, we adopted covariance-based struc-tural equation modelling (CB-SEM) for assessing theresearch model (Gefen, Rigdon, and Straub 2011)since it can provide us with complete resources for ver-ifying the hypotheses CB-SEM is used mostly when wehave an existing theory to test, whereas PLS-SEM (par-tial least squares structural equation modelling) isappropriate for theory building and prediction in theexploratory stage We proposed an integrated researchmodel based on the ELM and herd theory, which areestablished theories Therefore, CB-SEM is used in ourstudy It is a preferred method when the goal is theorytesting or theory confirmation Second, in the case of

Table 2.Respondents’ demographics

Percentage (%)