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Cited by 833AI in Service InteractionsCustomer Service Quality and LoyaltyTechnology Adoption and User Behaviour

Customer experiences in the age of artificial intelligence

Open access
Ameen, Nisreen, Tarhini, Ali, Reppel, Alexander, Anand, Amitabh·2021·Computers in Human Behavior
DOI 10.1016/j.chb.2020.106548PMC PMC7463275
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Abstract

Artificial intelligence (AI) is revolutionising the way customers interact with brands. There is a lack of empirical research into AI-enabled customer experiences. Hence, this study aims to analyse how the integration of AI in shopping can lead to an improved AI-enabled customer experience. We propose a theoretical model drawing on the trust-commitment theory and service quality model. An online survey was distributed to customers who have used an AI- enabled service offered by a beauty brand. A total of 434 responses were analysed using partial least squares-structural equation modelling. The findings indicate the significant role of trust and perceived sacrifice as factors mediating the effects of perceived convenience, personalisation and AI-enabled service quality. The findings also reveal the significant effect of relationship commitment on AI-enabled customer experience. This study contributes to the existing literature by revealing the mediating effects of trust and perceived sacrifice and the direct effect of relationship commitment on AI-enabled customer experience. In addition, the study has practical implications for retailers deploying AI in services offered to their customers.

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Crossref · 10.1016/j.chb.2020.106548

Cites

114 of 114 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 67 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    Trust and perceived sacrifice mediate the effects of perceived convenience, personalisation, and AI-enabled service quality on AI-enabled customer experience.

    “The findings indicate the significant role of trust and perceived sacrifice as factors mediating the effects of perceived convenience, personalisation and AI-enabled service quality.”
    Quote in the paperSubstantial claim
  • Findingown data

    Relationship commitment has a significant effect on AI-enabled customer experience.

    “The findings also reveal the significant effect of relationship commitment on AI-enabled customer experience.”
    Quote in the paperSubstantial claim
  • Findingown data

    Inner variance inflation factor values were lower than the threshold of 3.3.

    “The analysis showed that the inner variance inflation factor (VIF) values were lower than the threshold value of 3.3 (Petter, Straub, & Rai, 2007).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Petter, Straub, & Rai, 2007 source

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  • Findingown data

    Trust mediates the relationship between AI-enabled service quality and AI-enabled customer experience.

    “The results show that H7a is supported because trust mediates the relationship between AI-enabled service quality and AI-enabled customer experience as the direct effect without mediator is insignificant (t value = 2.52, CI = −0.17 to 0.15) (Baron & Kenny, 1986).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Baron & Kenny, 1986 source

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  • Findingown data

    Two types of AI-enabled customer experiences exist: hedonic and recognition.

    “The findings reveal that two types of AI-enabled customer experiences exist, which we have classified as hedonic and recognition, which extends the findings of recent studies on the influence of digital experiences on reputation and loyalty (Foroudi et al., 2018), customer experience with the integration of digital technologies (Parise, Guinan, & Kafka, 2016), and customer experiences in retailing”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Foroudi et al., 2018 source

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    • cites Parise, Guinan, & Kafka, 2016

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  • Classified as probably not a substantial claim. 29
  • May combine two claims. 25
  • The quote only partly states this claim. 2

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    All participants had used the brand's mobile app, virtual artist, and colour match feature, and all had used the virtual artist app for one to five years.

    “All participants in the sample had used the brand's mobile app, the virtual artist and the colour match feature and they also had all used the virtual artist app for between one and five years.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Exploratory factor analysis revealed a multi-factor structure, with the first factor accounting for 12% of the variance.

    “Exploratory factor analysis (EFA) revealed the existence of a multi-factor structure, with the first factor accounting for 12% of the variance in the sample.”
    Quote in the paperSubstantial claim

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    Findingown data

    95% of participants used the brand's chatbot service.

    “In addition, 95% of the participants used the brand's chatbot (Kik bot) service, while 5% had not used it.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Major sacrifices in AI-enabled services include lack of human interaction, loss of privacy, loss of control, time consumption, and irritation, and these can negatively affect AI-enabled service experiences.

    “The findings of our study identify major sacrifices consumers may face in AI-enabled services, such as a lack of human interaction, loss of privacy, loss of control, time consumption, and possible negative feelings of irritation, all of which can have a negative effect on AI-enabled service experiences.”
    Quote in the paperSubstantial claim

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    Findingnone given

    The study conceptualises AI-enabled customer experience in retail environments based on two dimensions: hedonic and recognition.

    “Second, our study conceptualises AI-enabled customer experience in retail environments based on two dimensions: Hedonic and recognition.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    All hypothesised relationships H1 to H6 were supported except H3b and H4a.

    “As shown in Fig. 3 , all hypothesised relationships are supported (H1 to H6), except H3b (perceived convenience - > perceived sacrifice: β-value = 0.09 and t value = 1.26) and H4a (personalisation - > trust: β-value = 0.02 and t value = 1.59).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Despite the absence of human interaction in AI-enabled services, human emotions and a feeling of recognition remain important aspects.

    “Despite the absence of human interaction in AI-enabled services, our results reveal that human emotions and a feeling of recognition remain important aspects.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Premisecitation

    The impact of issues related to AI-powered customer experiences needs to be better understood.

    “The impact of these and other potential issues related to AI-powered customer experiences need to be better understood (Malle, Scheutz, Arnold, Voiklis, & Cusimano, 2015; Shank, Graves, Gott, Gamez, & Rodriguez, 2019).”
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    • cites Malle et al., 2015

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    • cites Shank et al., 2019 source

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    Premisecitation

    There is a lack of research on how customers perceive AI technology in their shopping experience and how it leads to a more pleasant experience and stronger brand relationships.

    “As a result, there is a lack of research on how customers perceive AI technology as part of their shopping experience, and how this leads to a more pleasant experience and stronger relationships with brands (Shank et al., 2019; Wang, Molina & Sunder, 2020).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Shank et al., 2019 source

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    • cites Wang, Molina & Sunder, 2020

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    Premisecitation

    The more confident customers feel about a brand they purchase from, the more willing they are to engage in a long-term relationship with that brand.

    “Previous studies also show that the more confident customers feel about a brand they purchase from, the more they are willing to engage in a long-term relationship with that brand (Keiningham et al., 2017).”
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    • cites Keiningham et al., 2017 source

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    Premisecitation

    In AI-enabled services, two further non-monetary sacrifices are a lack of human interaction and potential social isolation, both of which can negatively impact customer experience.

    “In the context of AI-enabled services, two further non-monetary sacrifices have to be considered: A lack of human interaction and the potential for social isolation (Davenport, Guha, Grewal, & Bressgott, 2020), both of which can have a negative impact on the customer experience.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Davenport et al., 2020 source

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    Premisecitation

    Customers are willing to sacrifice control, power over choices, and privacy when they have no control over these processes, and this effect is likely to deepen as automated systems become more prevalent and complex.

    “Research suggests that customers are willing to sacrifice what they can control, power over choices, and privacy as they have no control over these processes (Anderson & Rainie, 2018; Anderson, Rainie, & Luchsinger, 2018), an effect that is likely to deepen as automated systems become more prevalent and complex.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Anderson & Rainie, 2018

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    • cites Anderson et al., 2018

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    Premisecitation

    Service quality is well understood in different contexts.

    “The conceptualisation of service quality in different contexts is well understood (e.g. Parasuraman, Zeithaml, & Berry, 1994; Collier & Bienstock, 2006; Scheidt & Chung, 2019; Suhartanto, Helmi Ali, Tan, Sjahroeddin, & Kusdibyo, 2019).”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites Parasuraman et al., 1994 source

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    • cites Collier & Bienstock, 2006

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    • cites Scheidt & Chung, 2019

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  • Classified as probably not a substantial claim.

    Interpretationcitation

    Increased trust in a brand and its technology leads to a better customer experience.

    “Applied to the context of retail marketing, this suggests that a higher level of trust in a brand and their technology increases the customer experience.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Multiple items were used to measure each factor.

    “We used multiple items to measure each factor.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Premisecitation

    Trust mediates the relationship between service quality and loyalty.

    “Previous studies also indicate that trust mediates the relationship between service quality and loyalty (Chou, 2014).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Chou, 2014

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  • Classified as probably not a substantial claim.

    Premisecitation

    Using a high-quality service reduces perceived sacrifice.

    “Previous studies indicate that the use a high-quality service decreases the perception of sacrifice (Stamenkov & Dika, 2019).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Stamenkov & Dika, 2019

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  • Classified as probably not a substantial claim.

    Premisecitation

    Technical and functional service quality affect how customers evaluate brands.

    “Previous studies acknowledge that technical and functional service quality affect the way customers evaluate brands (Chiou & Droge, 2006; Eisingerich & Bell, 2008).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Chiou & Droge, 2006

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    • cites Eisingerich & Bell, 2008

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  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationcitation

    An increase in convenience leads to a decrease in perceived sacrifice; convenience is negatively correlated with perceived sacrifice.

    “An increase in convenience thus leads to a decrease in perceived sacrifice, which means that convenience is negatively correlated with perceived sacrifice.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Trust and customer experience have been studied with trust as a mediator or as a direct factor.

    “Previous studies have investigated the relationship between trust and customer experience, either by considering trust as a mediator (e.g. Martin, Mortimer, & Andrews, 2015; Rose et al., 2012) or as a factor that has a direct effect on experience (Ling, Chai, & Piew, 2010).”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites Martin, Mortimer, & Andrews, 2015 source

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    • cites Rose et al., 2012 source

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    • cites Ling, Chai, & Piew, 2010 source

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  • Classified as probably not a substantial claim.

    Premisecitation

    Personalisation strategies that generate high positive attributions and low negative attributions are likely to strengthen customer commitment to a brand.

    “Finally, personalisation strategies that generate high positive attributions and low negative attributions and are likely to strengthen the commitment of customers towards a brand (Shen & Ball, 2009).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Shen & Ball, 2009 source

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  • May combine two claims.

    Interpretationreasoning

    Commitment leads customers to believe that no alternative brands offer similar benefits, making them less likely to shop from alternatives.

    “It directs customers to believe that there are no other alternative brands that would provide similar benefits, making them less likely to shop from alternative brands.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    PLS-SEM allows analysis of non-normally distributed data and is suitable for theory-testing and small sample sizes.

    “PLS-SEM allows the analysis of data that are not normally distributed, and it is suitable for theory-testing and handling small sample sizes (Hair et al., 2017).”
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    • cites Hair et al., 2017

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    Premisecitation

    Chatbots and other AI-assisted customer service tools are increasingly used as an automated and potentially efficient way to improve the customer journey.

    “Chatbots and other AI-assisted customer service tools are increasingly used as an automated and potentially efficient way of improving the customer journey (Treasure Data, 2019).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Treasure Data, 2019

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    Study design and conductown data

    Harman's single factor test was used to assess common method variance.

    “Harman's single factor test was employed to assess CMV.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Positive customer experiences affect commitment, and after initial commitment is built, commitment can influence later experiences.

    “Although previous studies highlight the significance of positive customer experiences on commitment (Iglesias, Singh, & Batista-Foguet, 2011; Lemon & Verhoef, 2016), more recent studies argue that once customers have gone through the initial experience of building brand commitment, this commitment can, in turn, influence subsequent experiences (Keiningham et al., 2017).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Iglesias, Singh, & Batista-Foguet, 2011

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    • cites Lemon & Verhoef, 2016 source

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    • cites Keiningham et al., 2017 source

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  • May combine two claims.

    Premisecitation

    If a service is personalised, customers may be less concerned about loss of privacy, which can improve their AI-enabled experience.

    “For example, if the service is personalised, customers may be less concerned about some level of loss of privacy (Li & Unger, 2012) which can lead to an improved perception of their AI-enabled experience.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Li & Unger, 2012

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    Premisecitation

    Customers have positive expectations and feel more comfortable when using a technology-enabled service they trust.

    “Customers start having positive expectations and feel more comfortable when using a technology-enabled service that they trust, in terms of trusting the brand, process and technology (Alsajjan & Dennis, 2006).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Alsajjan & Dennis, 2006

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  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    In AI-enabled customer experiences, customers who enjoy their personalised experience may feel less sensitive to what they are sacrificing, indicating a negative relationship between personalisation and perceived sacrifice.

    “In the context of AI-enabled customer experiences, customers who enjoy their personalised experience may feel less sensitive to what they are giving up (sacrificing) (Knight, 2018), which indicates a negative relationship between a high degree of personalisation and perceived sacrifice.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Knight, 2018

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  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    A pilot study with 50 responses was conducted, and minor changes were made to language and clarity.

    “A pilot study was conducted by collecting 50 responses to the questionnaire and a few minor changes were made in terms of language and clarity accordingly.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    When other information is absent, the type of technology and how a provider implements it can act as a proxy for the provider's character and help consumers form initial trust.

    “In the absence of other information, the type of technology and the way it is implemented by a service provider may act as a proxy into their character from a consumer perspective and help consumers establish an initial level of trust.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductcitation

    Purposive sampling was used with two criteria: participants had to be at least 18 years old and had to have used an AI-enabled service offered by the brand.

    “Following Malhotra and Galletta (1999, pp. 1–14), we used purposive (judgment) sampling based on two criteria: Individuals (1) must be at least 18 years old; and (2) have used an AI-enabled service offered by the brand.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Malhotra and Galletta (1999)

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  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    Common method variance bias exaggerates relationships in the theoretical model.

    “Common method variance (CMV) bias exaggerates relationships in the theoretical model.”
    Quote in the paperNot a substantial claim

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    Premisecitation

    Customers develop affective, normative, and calculative commitments when communicating with brands.

    “When communicating with brands, customers develop (a) affective, (b) normative and, (c) calculative commitments (Gustafsson, Johnson, & Roos, 2005; Keiningham et al., 2017; Verhoef et al., 2009).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Gustafsson, Johnson, & Roos, 2005 source

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    • cites Keiningham et al., 2017 source

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    • cites Verhoef et al., 2009 source

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Perceived sacrifice is a distinct factor from perceived service value.

    “Some studies position perceived sacrifice as a distinct factor from perceived service value (de Medeiros, Ribeiro, & Cortimiglia, 2016).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites de Medeiros, Ribeiro, & Cortimiglia, 2016

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  • Classified as probably not a substantial claim.

    Premisecitation

    Service quality affects perceived value, which is the trade-off between benefits and sacrifices in receiving a service.

    “Previous studies also acknowledge the effect of service quality on perceived value, which refers to the trade-off between benefits and sacrifices customers have to makein return for receiving a service (Gallarza, Arteaga, Chiappa, Gil-Saura, & Holbrook, 2017; Li & Shang, 2019).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Gallarza, Arteaga, Chiappa, Gil-Saura, & Holbrook, 2017

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    • cites Li & Shang, 2019

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  • Classified as probably not a substantial claim.

    Interpretationreasoning

    Trust strengthens the effect of convenience on customer experience.

    “As a result, the presence of trust strengthens the effect convenience has on the customer experience.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    A total of 434 unique responses were collected between February and April 2020.

    “A total of 434 unique responses were collected between February and April 2020.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Consumers can become more interested in interacting with brands if they have positive interactions and strong relationships, which can increase commitment.

    “Morgan and Hunt (1994) explain that consumers can become more interested in interacting with brands if they experience positive interactions and build strong relationships with them, which can lead them to be more committed towards these brands.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Morgan and Hunt (1994) source

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  • May combine two claims. The quote only partly states this claim.

    Study design and conductown data

    Discriminant validity was supported by the Fornell-Larcker comparison and by heterotrait-monotrait values.

    “From Table 2, the results suggest strong evidence of discriminant validity.”
    • Also stated in: § Assessment of the measurement model · plain
      “Based on the results shown in Table 3 , it can be concluded that discriminant validity was met.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    Trust matters to retailers when they introduce technologies, and it may matter even more for AI deployment.

    “Consumer trust is an important factor for retailers to consider when introducing technologies, but it may be even more important when deploying AI.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). The quote only partly states this claim.

    Interpretationown data

    A balanced approach to human interaction can increase relationship commitment.

    “Accordingly, retails should aim for a balanced approach in terms of human interaction, for example, through carefully personalised experiences that are accompanied by a well-trained customer support team. This can increase relationship commitment, which our study has shown has a significant effect on how consumers perceive their AI-enabled experiences.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    The integration of AI causes a shift in how consumers perceive their experiences and the factors they consider important for success.

    “Our findings reveal how the integration of AI causes a shift in how consumers perceive their experiences, and the factors they consider to be important for the success of these experiences.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    The study's analysis was based on 434 responses, which limits the generalisability of the findings.

    “In addition, we collected data based on 434 responses which were included in the analysis. However, future research can collect and analyse data using a larger sample size to increase the opportunity of generalising the findings.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    When studying AI-enabled services, the context and dimensions of established constructs such as service quality may have to be adjusted.

    “Moreover, when studying AI- enabled services, the context and dimensions of established constructs such as service quality may have to be adjusted in response to the unique nature of AI technology.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationown data

    The study's results may not extend beyond consumers of a single pioneering beauty brand's AI-enabled services to other retailers and industries.

    “Furthermore, this study focuses on customers using the AI-enabled services of one pioneering brand in the beauty industry, future studies could investigate different retailers and industries.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductcitation

    Average variance extracted values were above 0.5, indicating convergent validity.

    “In terms of convergent validity, the average variance extracted (AVE) values were above the threshold value of 0.5 (Fornell & Larcker, 1981).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Fornell & Larcker, 1981 source

      Not checked in the free run.

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  • The paper relies on this without support.

    Hedonism and a need for autonomy motivate customers.

    “The proposed model takes into account that customers are motivated by hedonism and a need for autonomy.”

  • The paper relies on this without support.

    The decision to accept sacrifices is situation dependent.

    “The decision to accept sacrifices is situation dependent.”

  • The paper relies on this without support.

    AI-enabled services tend to be highly structured, with the sequence of steps determined by technology requirements rather than user needs.

    “AI-enabled services tend to be highly structured with the sequence of steps a customer has to go through often determined by the requirements of the technology rather than the needs of the user.”

  • The paper relies on this without support.

    The lack of human assistance in AI-enabled services may create obstacles for customers, especially those without prior experience or those who take longer to use these services comfortably.

    “Finally, the lack of human assistance (human agency) in AI-enabled services may create obstacles for customers, especially those without prior experience or those who may take longer to use these services comfortably.”

  • The authors state this as an assumption.

    Customer convenience would increase with AI-enabled services because they can be used anytime and anywhere.

    “With AI-enabled services, it can be assumed that customer convenience would increase as they can be used anytime and anywhere.”

  • The paper relies on this without support.

    The quality of AI-enabled services depends largely on the amount and quality of personal information a brand can collect about customers.

    “The quality of AI-enabled services depends to a large extend on the amount and quality of personal information a brand is able to collect about customers.”

  • The paper relies on this without support.

    AI-enabled services are much more scalable than traditional services and can serve many customers simultaneously, though they likely exchange past biases for new ones.

    “While it is much more likely that AI-enabled services exchange past biases for new ones, they are much more scalable than traditional services and have the potential to serve a high number of customers simultaneously.”

  • The paper relies on this without support.

    AI-enabled services often do not involve human interaction.

    “As AI-enabled services often do not involve human interaction, customers perception as a high-quality service is critical for reducing the effect of perceived sacrifices, particularly those related to a loss of human support and control.”

  • The paper relies on this without support.

    AI-enabled services offer a significantly higher level of personalisation to their users.

    “In addition, AI-enabled services offer a significantly higher level of personalisation to their users.”

  • The paper relies on this without support.

    Personalisation is based on the collection and constant analysis of customers' data.

    “Since personalisation is based on the collection and constant analysis of customers’ data.”

  • The authors state this as an assumption.

    No prior study has empirically tested trust and perceived sacrifice together as mediators in a holistic model of AI-enabled customer experience.

    “Previous studies also highlight the importance of trust and the sacrifices users may have to make when using AI-enabled services (e.g. Davenport et al., 2020), although both factors have to the best of our knowledge yet to be empirically tested as part of a holistic theoretical model.”

  • The paper relies on this without support.

    The virtual artist app platform shows a reasonable level of interaction by customers.

    “As the virtual artist app platform shows a reasonable level of interaction by customers, using this platform provided a higher level of access to the target participants.”

  • The paper relies on this without support.

    This work is one of the earliest to examine consumer experience with AI-enabled services.

    “Our study is among the first to focus on customers’ experience in the context of AI-enabled services.”
13 of 13

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • This kind of service has a higher degree of fit with consumers, which can enhance consumers' loyalty and satisfaction more effectively.

    “This kind of service has a higher degree of fit with consumers, which can enhance consumers' loyalty and satisfaction more effectively [134].”

    From The Impact of AI-Personalized Recommendations on Clicking Intentions: Evidence from Chinese E-Commerce (2025) · cites this paper as [134]

    The Personalisation section (page unavailable) says tailoring services to customers’ needs can strengthen relationship commitment, and the Introduction says AI-enabled interactions may increase customer satisfaction. These passages support the general idea, but the paper does not establish that such services improve loyalty and satisfaction more effectively than other services. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Trust mediation is marked supported despite a zero-crossing interval

    Major

    The reported 95% confidence interval for the indirect effect includes zero, yet the table marks the trust-mediated service-quality effect as supported. That conflicts with the paper's stated criterion for mediation.

  • Personalisation sacrifice mediation has a zero-crossing interval

    Major

    The reported indirect-effect interval includes zero, although the table marks the perceived-sacrifice mediation as supported. This conflicts with the paper's own mediation criterion.

  • Service-quality sacrifice mediation has a zero-crossing interval

    Major

    The indirect-effect interval extends from a negative to a positive value, but the table marks this mediation as supported. Under the stated confidence-interval criterion, this result does not establish mediation.

  • An indirect-effect statistic is described as a direct effect

    Minor

    The narrative calls t = 2.52 with CI −0.17 to 0.15 the direct effect without a mediator. Table 4 places those figures in the indirect-effect column; its direct effect without a mediator is reported as 2.19.

4 of 4

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Authors
Ameen, N., Tarhini, A., Reppel, A., & Anand, A.
Affiliations
  • Royal Holloway University of London (GB)
  • Sultan Qaboos University (OM)
  • SKEMA Business School (FR)
Corresponding author
Ameen, Nisreen
ORCID
Venue
Computers in Human Behavior
Publisher
Elsevier BV
Published
2021-01
Volume
114
Pages
106548
ISSN
0747-5632 (ISSN-L)
DOI
10.1016/j.chb.2020.106548
PMC
PMC7463275
OpenAlex
W3082622239
Semantic Scholar
7c3d39144f8890a58b1fdfcf7a3446dd62a98850
Cited by
833
References
114
Citations by year
2026: 166 · 2025: 222 · 2024: 198 · 2023: 117 · 2022: 85 · 2021: 44 · 2020: 1
FWCI
24.28
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers in Human Behavior (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • HAL (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Keywords
artificial intelligence, customer experience, trust, perceived sacrifice, relationship commitment, perceived convenience, personalization, service quality, cosmetics brands, online shopping, mediating effect

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-04.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-04.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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