An empathy lens into peer service providers: Personal versus commercial hosts

Shuqair, Saleh; Pinto, Diego Costa; Mattila, Anna S.

doi:10.1016/j.ijhm.2021.103073

Cited by 14 publications

(8 citation statements)

References 86 publications

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“…We examine how loyalty and satisfaction can be predicted using AI tools. Overall, our findings extend prior studies on consumer loyalty in hospitality [54,55] by showing how loyalty can be predicted. Therefore, the current study contributes to emerging studies on AI's impact on data prediction [56,57].…”

Section: -Discussionsupporting

confidence: 86%

“…The TPOT takes longer with more data. By default, the TPOT settings include 100 generations and 100 as the population size, producing 10,000 model configurations to evaluate with 10-fold cross-validation, which means that 100,000 models are fitted and evaluated on the training data in one grid search [55]. Cross-validation affects how long TPOT takes to evaluate models, as each model will be trained k times, where k equals the number of cross-validation splits [56].…”

Section: -4-institutional Review Board Statementmentioning

confidence: 99%

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The Benefits of Automated Machine Learning in Hospitality: A Step-By-Step Guide and AutoML Tool

et al. 2022

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The manuscript presents a tool to estimate and predict data accuracy in hospitality by means of automated machine learning (AutoML). It uses a tree-based pipeline optimization tool (TPOT) as a methodological framework. The TPOT is an AutoML framework based on genetic programming, and it is particularly useful to generate classification models, for regression analysis, and to determine the most accurate algorithms and hyperparameters in hospitality. To demonstrate the presented tool’s real usefulness, we show that the TPOT findings provide further improvement, using a real-world dataset to convert key hospitality variables (customer satisfaction, loyalty) to revenue, with up to 93% prediction accuracy on unseen data. Doi: 10.28991ESJ-2022-06-06-02 Full Text: PDF

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Section: -Discussionsupporting

confidence: 86%

Section: -4-institutional Review Board Statementmentioning

confidence: 99%

The Benefits of Automated Machine Learning in Hospitality: A Step-By-Step Guide and AutoML Tool

et al. 2022

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“…However, communal relationships are fundamental to generating a sense of unconditional social connection between brands and consumers (Clark and Mills, 2012;Shuqair et al, 2021;Zaki et al, 2021), working as stronger cues for customerbrand relationships and diluting the effect of crowdsourcing cues. Consumers with communal brand relationships view their interactions as more personal and closer (Aggarwal, 2004;Aggarwal and Larrick, 2012;Clark and Mills, 1993;Costello and Reczek, 2020;Wan et al, 2011).…”

Section: The Moderating Role Of Relationship Normsmentioning

confidence: 99%

Does crowdsourcing necessarily lead to brand engagement? The role of crowdsourcing cues and relationship norms on customer-brand relationships

Herter

Shuqair²,

Pinto³

et al. 2023

JPBM

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Purpose This paper aims to examine how the relationship norms established between customers and brands influence customer perceptions of crowdsourcing (vs firm-generated) cues. Design/methodology/approach Four studies (N = 851) examine the moderating role of relationship norms on product labeling cues (crowdsourcing vs firm-generated) effects on brand engagement, and the underlying mechanism of self-brand connection. Findings The findings suggest that crowdsourcing (vs firm-generated) cues lead to higher brand engagement (Studies 1A–1B), mediated by self-brand connection (Studies 2–3). In addition, relationship norms moderate the effects (Study 3), such that under exchange brand relationships crowdsourcing (vs firm-generated) cues yield higher brand engagement, whereas communal brand relationships reverse such effects. Practical implications The findings provide valuable managerial implications by highlighting the importance of using relationship norms as diagnostic cues to successfully implement crowdsourcing initiatives. Originality/value This research adds to the customer-brand relationship literature by revealing an accessibility-diagnosticity perspective of consumers’ reactions to crowdsourcing (vs firm-generated) cues.

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“…When hosts rent space in their homes, they bring the social domain into their private domain ( Molz, 2014 ) that can generate conflicts that need to be resolved. By renting the entire home or opening the home to strangers through shared facilities, hosts (personal peer vs. commercial peer providers— Shuqair et al, 2021a ) provide hospitableness through, for example, sharing meals, providing advice and interacting with guests. Social closeness (or social distance) with hosts affects the customer experience ( So et al, 2019 ).…”

Section: Resultsmentioning

confidence: 99%

“…While conflicts in the host-guest relationship is not uncommon in network hospitality provision such as Airbnb ( Sthapit and Jiménez-Barreto, 2019 ; Shuqair et al, 2021a ), the extent of social distance or closeness ( So et al, 2019 ) that occurs in the private-social domain of hospitality provision can both enhance and diminish host wellbeing. As there are insufficient guidelines provided by Airbnb around the extent of hosts and guests interactions online and face-to-face, it becomes the domain of the host to determine the nature and level of interactions with guests that will fulfill the latter’s expectations.…”

Section: Discussionmentioning

confidence: 99%

Exploring Airbnb Host Wellbeing and Host-Guest Conflicts in Network Hospitality

Ozanne

Prayag

2022

Front. Psychol.

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Despite a plethora of studies examining hosting experiences of Airbnb guests, the wellbeing of hosts has received limited attention. Drawing on both top-down and bottom-up theories of wellbeing, we explore the different ways in which Airbnb enhances or diminishes host wellbeing using a multidimensional lens (material, relational and subjective wellbeing). Data is collected from in-depth interviews with twenty-two Airbnb hosts. We also identify tensions and conflicts in the host-guest relationship using the three interactional hospitality domains of commercial, social and private. Through a deductive process, we find that participating in Airbnb both enhances and diminishes the material, relational and subjective aspects of wellbeing for hosts. Inductively, we find that a lack of territorial boundaries in shared accommodation contribute to conflicts that reduce wellbeing. This exemplifies a tension that exists in the private-commercial domain of network hospitality provision. We provide implications for peer-to-peer accommodation providers on developing the managerial skills of hosts, and helping hosts set realistic expectations around hosting to reduce the conflicts and tensions that arise from the intersectionality of the various Airbnb hospitality domains in such a way that the wellbeing of hosts is enhanced.

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An empathy lens into peer service providers: Personal versus commercial hosts

Cited by 14 publications

References 86 publications

The Benefits of Automated Machine Learning in Hospitality: A Step-By-Step Guide and AutoML Tool

The Benefits of Automated Machine Learning in Hospitality: A Step-By-Step Guide and AutoML Tool

Does crowdsourcing necessarily lead to brand engagement? The role of crowdsourcing cues and relationship norms on customer-brand relationships

Exploring Airbnb Host Wellbeing and Host-Guest Conflicts in Network Hospitality

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