A data-driven approach to measure restaurant performance by combining online reviews with historical sales data

Because tourism destinations are difficult to assess in certain standard aspects, the factors that contribute to the helpfulness of reviews remain largely unknown. Moreover, the helpfulness of online reviews has not been explored in terms of the interaction between language style (high- vs. low-cognitive) and attraction type (hedonic vs. utilitarian). Hence, this study examines the impact of language style on the helpfulness of an online review of an attraction, depending on the type of attraction and the meaning of the destination. This study’s data included 8,032 reviews of four attractions (2 hedonic x 2 utilitarian), drawn from TripAdvisor in two different meanings of destinations. Specifically, our findings indicate that when a reviewer posts a utilitarian attraction of the destination, high-cognitive language is perceived to be more helpful. First, we discuss the theoretical contribution of our study using cognitive fit theory, and then provide the study’s managerial implications.

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A data-driven approach to exploring similarities of tourist attractions through online reviews

Journal of Location Based Services ◽

10.1080/17489725.2018.1493548 ◽

2018 ◽

Vol 12 (2) ◽

pp. 94-118 ◽

Cited By ~ 4

Author(s):

Grant McKenzie ◽

Benjamin Adams

Keyword(s):

Online Reviews ◽

Data Driven ◽

Tourist Attractions ◽

Data Driven Approach

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Lifestyles in Amazon: Evidence from online reviews enhanced recommender system

International Journal of Market Research ◽

10.1177/1470785319844146 ◽

2019 ◽

Vol 62 (6) ◽

pp. 689-706 ◽

Cited By ~ 1

Author(s):

Yinghui Huang ◽

Hui Liu ◽

Weiqing Li ◽

Zichao Wang ◽

Xiangen Hu ◽

...

Keyword(s):

Personality Traits ◽

Recommender System ◽

Consumer Preference ◽

Addition Product ◽

Online Reviews ◽

Data Driven ◽

Big Five Personality ◽

Predicting Performance ◽

Wide Range ◽

Data Driven Approach

Online lifestyles have been shown to reflect and affect consumers’ preferences across a wide range of online scenarios. In the context of e-commerce, it still remains unclear whether online lifestyles are practically influential in predicting consumers’ purchasing preferences across different product categories, especially considering its potential influence over the widely used personality traits. In this study, we provide the first, to the best of our knowledge, quantitative demonstration of online lifestyles in predicting consumers’ online purchasing preferences in e-commerce by using a data-driven approach. We first construct an online lifestyles lexicon including seven distinct dimensions using text mining approaches based on consumers’ language use behaviors. We then incorporate the lexicon in a typical e-commerce recommender system to predict consumers’ purchasing preferences. Experimental results on Amazon Review Dataset show that online lifestyles and all its subdimensions significantly improve preference predicting performance and outperform the widely used Big Five personality traits as a whole. In addition, product types significantly moderate the influence of online lifestyle on consumer preference. The strong empirical evidence indicates that the big e-commerce consumer data facilitates more specialized market psychographic segmentation, which advances data-driven marketing decision-making.

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Data-Driven Approach to Dual Service Failure Monitoring From Negative Online Reviews: Managerial Perspective

SAGE Open ◽

10.1177/2158244020988249 ◽

2021 ◽

Vol 11 (1) ◽

pp. 215824402098824

Author(s):

Junegak Joung ◽

Ki-Hun Kim ◽

Kwangsoo Kim

Keyword(s):

Latent Dirichlet Allocation ◽

Service Failure ◽

Low Cost ◽

Threshold Value ◽

Online Reviews ◽

Data Driven ◽

Service Failures ◽

Data Driven Approach ◽

Failure Monitoring ◽

Managerial Perspective

Monitoring of dual service failures (e.g., trends in service failures and consecutive service failures) in business is emphasized for service quality management. Previous studies analyzing negative online reviews to conduct dual service failure monitoring from a managerial perspective are scarce. Numerous negative online reviews are useful sources for dual service failure monitoring because they can be easily collected at a low cost. This article proposes a data-driven approach to monitor service failure trends and consecutive service failures from negative online reviews. In the proposed approach, first a classifier is developed to categorize newly collected negative reviews into service failures by Latent Dirichlet allocation. Subsequently, a threshold value is provided to identify a new type of service failure, which was not achieved previously using a control chart. Finally, the probability of consecutive service failures is obtained by association rule mining. A case study of Uber is conducted to validate the proposed approach. The results exhibit that the proposed approach can perform dual service failure monitoring. This study can increase marketing intelligence for dynamic management of service failure and allow rapid responses to service failures.

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