Search results

This research investigates consumer intention to follow online community advice. Applying the technology acceptance model (TAM) to the context of online restaurant communities, the study empirically examines the effects of perceived usefulness, perceived ease of use, attitude and trust on the intention to follow online advice.

The data were collected from 360 members of online restaurant communities on Facebook and analyzed using structural equation modeling (SEM).

The findings revealed that trust, perceived usefulness and attitude are key predictors of the intention to follow online restaurant community advice.

Extant research on the influence of online reviews on consumer behavior in the restaurant industry has largely focused on the characteristics of the review, reviewers or readers. Moreover, other studies have investigated consumers' motivations to write online restaurant reviews. This study, however, takes a different approach and examines what drives consumers to follow the advice from online restaurant communities.

Digitalization in marketing activities has made it easier for people to make purchase decision. This platform encourages every firm to optimize digitalization as part of its marketing strategy. Optimization of attractive digital marketing involves advertising attractiveness, influencer marketing and online customer reviews. This study aims to investigate advertising attractiveness, influencer marketing and online customer reviews on purchase decision.

The study was conducted with a quantitative approach. A total of 120 respondents were involved in this study by using convenience sampling techniques in data collection. Multiple linear regression was used to analyze the data.

The results of the study show that influencer marketing and online customer reviews have an impact on online purchase decision. Meanwhile, advertising attractiveness does not show any influence on purchase decision.

Despite the start-ups have modified the website by increasing the content to make it more informative, it seems that customers are not interested in making a purchase. Therefore, notwithstanding the role of website attractiveness, the use of physical attractiveness is still considered an effective way to encourage customers to make purchasing decisions. In this way, a firm needs to make adjustments between the customers' personality, lifestyle and attitudes and endorsers.

This study developed previous empirical studies which a positive relationship between advertising attractiveness, influencer marketing, online customer reviews and purchase decision. The development of the model was carried out by elaborating variable indicators. In addition, the source of increasing credibility was not based on physical attractiveness, but rather emphasizes the website quality.

The growing complexity of consumer engagement (CE) due to the impact of Internet of things (IoT) has been attracting significant attention from both academics and industry practitioners especially in recent times. Hence, understanding this phenomenon remains very crucial to the body of knowledge. This study conducted a systematic review on IoT and CE with the aim of proposing future research opportunities using the TCCM model.

Extant literature studies were systematically examined by sourcing high ranking ABS journals from EBSCO, ScienceDirect and Emerald. A total of 58 articles were included in the final analysis of this research.

The analysis established the need to conduct more research on CE due to the impact of new technological implementation in retail. The results further suggest the need for extensive research across African countries and emerging markets to enable broader empirical generalizations of research outcomes. Using the TCCM framework, the authors indicated directions for future empirical research.

This study exposes the current trends in CE and IoT. The results and analysis are both compelling and verifiable, hence, establishing a firm base of reference for future research in related fields.

The purpose of this study is to summarize the available pool of literature on service quality to identify different dimensions of service quality in the healthcare industry and understand how it is measured. The study attempts to explore the research gaps in the literature about different service quality dimensions and patient satisfaction.

A systematic literature review process was followed to achieve the objectives of the study. Various inclusion and exclusion criteria were used to select relevant research articles from 2000–2020 for the study, and a total of 100 research articles were selected.

The study identified 41 different dimensions of healthcare service quality measurement and classified these dimensions into four categories, namely servicescape, personnel, hospital administration and patients. It can be concluded that SERVQUAL is the most widely used service quality measurement tool.

The study identified that a majority of the researchers deduced a positive relationship between SERVQUAL dimensions and the quality of healthcare services. The findings of study will assist hospital executives in formulating effective strategies to ensure that patients receive superior quality healthcare services.

The authors investigate the varying impact of three categories of conflicting consumer reviews (i.e. conflicting opinions on attributes of a product item, conflicting ratings of an item and the intensity of conflicting reviews of an item) on the potential customers' perceived informativeness, which is expected to affect the perceived correct purchase.

To test their proposed hypotheses, the authors conducted an experiment using a 2 × 2 × 2 factorial design for each conflict type comprising two levels (low vs high).

The results of this study found that conflicting opinions on product attributes can enhance potential customers' perceptions of informativeness and subsequent correct purchase decisions while conflicting ratings and the intensity of conflicting reviews can diminish potential customers' perceptions of informativeness. In addition, conflicting ratings negatively moderate the effect of conflicting attributes on perceived informativeness such that the positive effect of conflicting attributes on perceived informativeness will be less prominent when conflicting ratings are present (vs absent).

While potential customers are browsing product descriptions, reviews and comments from other purchasers are also playing a role in influencing a potential customer's purchase decision. However, given the different experiences and temperaments of individuals, the subjective remarks and ratings of individuals are sometimes inconsistent or even conflicting, which can lead to confusion among potential customers. The authors categorize the positive or negative effects of the three conflicting reviews based on the two dimensions of ease of capture and product diagnosticity. The findings can help platforms optimize the display of product reviews to help potential customers make more accurate purchase decisions.

The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.

A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.

The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.

The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.

This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.

Global adoption of the internet and mobile usage results in a huge variation in the cultural backgrounds of consumers who generate and consume electronic word-of-mouth (eWOM). Unsurprisingly, a research trend on cross-cultural eWOM has emerged. However, there has not been an attempt to synthesize this research topic. This paper aims to bridge this gap.

This research paper conducts a systematic literature review of the current research findings on cross-cultural eWOM. Journal articles published from 2006 to 2021 are included. This study then presents the key issues in the extant literature and suggests potential future research.

The findings show that there has been an upward trend in the number of publications on cross-cultural eWOM since the early 2010s, with a relatively steeper increase toward 2020. The findings also synthesize cross-cultural eWOM research into four elements and suggest potential future research avenues.

To the best of the authors’ knowledge, there is currently no exhaustive/integrated review of cross-cultural eWOM research. This research fills the need to summarize the current state of cross-cultural eWOM literature and identifies research questions to be addressed in the future.

El boca a boca electrónico cross-cultural: una revisión sistemática de la literatura

La adopción global de Internet y los móviles da lugar a una enorme diferencia en el origen cultural de los consumidores que generan y consumen el boca a boca electrónico (eWOM). No es de extrañar que haya surgido una tendencia de investigación sobre el eWOM transcultural. Sin embargo, no se ha intentado sintetizar este tema de investigación. El objetivo de este artículo es subsanar esta carencia.

Este trabajo de investigación realiza una revisión bibliográfica sistemática de las investigaciones realizadas sobre eWOM transcultural. Se incluyen artículos de revistas publicados desde 2006 hasta 2021. A continuación, el estudio presenta las cuestiones clave de la literatura existente y sugiere posibles investigaciones futuras.

Los resultados muestran que ha habido una tendencia al alza en el número de publicaciones sobre eWOM intercultural desde principios de la década de 2010, con un aumento relativamente creciente hacia 2020. Los resultados también sintetizan la investigación sobre eWOM intercultural en cuatro elementos y sugieren posibles vías de investigación futuras.

Actualmente no existe una revisión exhaustiva/integrada de la investigación sobre el eWOM cross-cultural. Esta investigación satisface la necesidad de resumir el estado actual de la literatura sobre eWOM cross-cultural e identifica las cuestiones de investigación que deben abordarse en el futuro.

跨文化电子口碑研究：系统性文献回顾

摘要

在互联网全球化以及移动手机的广泛使用的背景下, 不同文化背景的消费者都在贡献电子口碑（eWOM）。这使得电子口碑存在文化差异。然而, 还没有人试图对这个研究课题进行综合分析。本文的目的就是要弥补这一空白。

本研究论文对目前关于跨文化eWOM的研究成果进行了系统的文献回顾。包括2006年至2021年发表的期刊文章。然后, 本研究提出了现有文献中的关键问题, 并提出了潜在的未来研究。

研究结果显示, 自2010年初以来, 关于跨文化eWOM的出版物数量呈上升趋势, 到2020年时增幅相对较大。研究结果还总结了跨文化eWOM研究的四个要素, 并提出了潜在的未来研究途径。

目前还没有关于跨文化eWOM研究的详尽/综合的回顾。这项研究填补了总结跨文化电子WOM文献现状的需要, 并确定了未来要解决的研究问题。

Engaging in webcare, i.e. responding to online reviews, can positively affect consumer attitudes, intentions and behavior. Research is often scarce or inconsistent regarding the effects of specific webcare strategies on business performance. Therefore, this study tests whether and how several webcare strategies affect hotel bookings.

We apply machine learning classifiers to secondary data (webcare messages) to classify webcare variables to be included in a regression analysis looking at the effect of these strategies on hotel bookings while controlling for possible confounds such as seasonality and hotel-specific effects.

The strategies that have a positive effect on bookings are directing reviewers to a private channel, being defensive, offering compensation and having managers sign the response. Webcare strategies to be avoided are apologies, merely asking for more information, inviting customers for another visit and adding informal non-verbal cues. Strategies that do not appear to affect future bookings are expressing gratitude, personalizing and having staff members (rather than managers) sign webcare.

These findings help managers optimize their webcare strategy for better business results and develop automated webcare.

We look into several commonly used and studied webcare strategies that affect actual business outcomes, being that most previous research studies are experimental or look into a very limited set of strategies.

Background: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations, travel demand, safety-performance evaluation, and maintenance. Regular updates help to determine traffic patterns for decision-making. Unfortunately, the luxury of having permanent recorders on all road segments, especially low-volume roads, is virtually impossible. Consequently, insufficient AADT information is acquired for planning and new developments. A growing number of statistical, mathematical, and machine-learning algorithms have helped estimate AADT data values accurately, to some extent, at both sampled and unsampled locations on low-volume roadways. In some cases, roads with no representative AADT data are resolved with information from roadways with similar traffic patterns.

Methods: This study adopted an integrative approach with a combined systematic literature review (SLR) and meta-analysis (MA) to identify and to evaluate the performance, the sources of error, and possible advantages and disadvantages of the techniques utilized most for estimating AADT data. As a result, an SLR of various peer-reviewed articles and reports was completed to answer four research questions.

Results: The study showed that the most frequent techniques utilized to estimate AADT data on low-volume roadways were regression, artificial neural-network techniques, travel-demand models, the traditional factor approach, and spatial interpolation techniques. These AADT data-estimating methods' performance was subjected to meta-analysis. Three studies were completed: R squared, root means square error, and mean absolute percentage error. The meta-analysis results indicated a mixed summary effect: 1. all studies were equal; 2. all studies were not comparable. However, the integrated qualitative and quantitative approach indicated that spatial-interpolation (Kriging) methods outperformed the others.

Conclusions: Spatial-interpolation methods may be selected over others to generate accurate AADT data by practitioners at all levels for decision making. Besides, the resulting cross-validation statistics give statistics like the other methods' performance measures.

Digital resources such as smart applications reviews and online feedback information are important sources to seek customers’ feedback and input. This paper aims to help government entities gain insights on the needs and expectations of their customers. Towards this end, we propose an aspect-based sentiment analysis hybrid approach that integrates domain lexicons and rules to analyse the entities smart apps reviews. The proposed model aims to extract the important aspects from the reviews and classify the corresponding sentiments. This approach adopts language processing techniques, rules, and lexicons to address several sentiment analysis challenges, and produce summarized results. According to the reported results, the aspect extraction accuracy improves significantly when the implicit aspects are considered. Also, the integrated classification model outperforms the lexicon-based baseline and the other rules combinations by 5% in terms of Accuracy on average. Also, when using the same dataset, the proposed approach outperforms machine learning approaches that uses support vector machine (SVM). However, using these lexicons and rules as input features to the SVM model has achieved higher accuracy than other SVM models.