ABSTRACT
Purpose: The purpose of this paper is exploring the effects of segment dynamic and temporal dynamic triggered by the COVID-19 pandemic on classifying service quality attributes, thereby formulating improvement strategies to satisfy customers and respond to threats. Design/methodology/approach: Given the dynamics of the attractive quality theory, this paper designs a framework with four phases by embedding techniques of text mining and deep learning based on evidence from online reviews. Findings: This paper figures out dynamics of service quality attributes for distinct segments and their dynamic proportion along with different stages of the pandemic. Another finding demonstrates segment dynamic and temporal dynamic effects of sentiments toward service quality attributes on customer satisfaction under the impacts of pandemic. Classification results and improvement strategies are derived for varying segments at different pandemic situations. Practical implications: This paper reveals dynamic effects on classifying service quality attributes, which contributes to assisting hospitality practitioners from different segments in improving service quality when facing with the challenges of crisis and potential risks. Originality/value: Given hospitality industry is time- and segment-sensitive, the authors achieve the quantification of dynamics of attractive quality theory and extend it into hospitality marketing and crisis management from the perspective of dynamics with evidence from online reviews. © 2022, Emerald Publishing Limited.
ABSTRACT
Purpose The purpose of this paper is exploring the effects of segment dynamic and temporal dynamic triggered by the COVID-19 pandemic on classifying service quality attributes, thereby formulating improvement strategies to satisfy customers and respond to threats. Design/methodology/approach Given the dynamics of the attractive quality theory, this paper designs a framework with four phases by embedding techniques of text mining and deep learning based on evidence from online reviews. Findings This paper figures out dynamics of service quality attributes for distinct segments and their dynamic proportion along with different stages of the pandemic. Another finding demonstrates segment dynamic and temporal dynamic effects of sentiments toward service quality attributes on customer satisfaction under the impacts of pandemic. Classification results and improvement strategies are derived for varying segments at different pandemic situations. Practical implications This paper reveals dynamic effects on classifying service quality attributes, which contributes to assisting hospitality practitioners from different segments in improving service quality when facing with the challenges of crisis and potential risks. Originality/value Given hospitality industry is time- and segment-sensitive, the authors achieve the quantification of dynamics of attractive quality theory and extend it into hospitality marketing and crisis management from the perspective of dynamics with evidence from online reviews.
ABSTRACT
Network Data Envelopment Analysis (NDEA) has the potential to be usefully combined with Big Data sets. We first discuss the DEA technology coefficient matrix which incorporates certain Big Data characteristics including volume, velocity, and variety. In addition, we review potential problems that can arise in using DEA to estimate producer’s performance relative some true, but unobserved technology, and proposed aggregation methods to reduce the curse of dimensionality. The various form that NDEA models can take, including dynamic effects, spillovers between producers, joint production of desirable and undesirable outputs, and the reallocation of inputs, across time, to optimize production. An example of the use of NDEA is offered for the Covid Pandemic in the US. We find that an optimal reallocation of tests for Covid could have averted 10,800 deaths. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.