Search results
1 – 4 of 4Eloy Gil-Cordero, Pablo Ledesma-Chaves, Rocío Arteaga Sánchez and Ari Melo Mariano
The aim of this study is to examine the behavioral intention (BI) to adopt the Coinbase Wallet by Spanish users.
Abstract
Purpose
The aim of this study is to examine the behavioral intention (BI) to adopt the Coinbase Wallet by Spanish users.
Design/methodology/approach
A survey was administered to individuals residing in Spain between March and April 2021. There were 301 questionnaires analyzed. This research applies a new predictive model based on technology acceptance model (TAM) 2, the unified theory of acceptance and use of technology (UTAUT) model, the theory of perceived risk and the commitment trust theory. A mixed partial least squares structural equation modeling (PLS-SEM)/fuzzy-set qualitative comparative analysis (fsQCA) methodology was employed for the modeling and data analysis.
Findings
The results showed that all the variables proposed have a direct and positive influence on the intention to use a Coinbase Wallet. The findings present clear directions for traders, investors and academics focused on improving their understanding of the characteristics of these markets.
Originality/value
First, this study addresses important concerns relating to the adoption of crypto-wallets during the global pandemic. Second, this research contributes to the existing literature by adding electronic word of mouth (e-WOM), trust, web quality and perceived risk as new drivers of the intention to use the Coinbase Wallet, providing unique and innovative insights. Finally, the study offers a solid methodological contribution by integrating linear (PLS) and nonlinear (fsQCA) techniques, showing that both methodologies provide a better understanding of the problem and a more detailed awareness of the patterns of antecedent factors.
Details
Keywords
Pimsuporn Poyoi, Ariadna Gassiot-Melian and Lluís Coromina
Posting and sharing about food on social media has surged in popularity amongst younger generations such as Millennials and Generation Z. This study aims to analyse and compare…
Abstract
Purpose
Posting and sharing about food on social media has surged in popularity amongst younger generations such as Millennials and Generation Z. This study aims to analyse and compare food-tourism sharing behaviour on social media across generations. First, this study specifically investigates the factors influencing the intention to share food experiences on social media; second, it examines the impact of sharing intention on actual behaviour and loyalty; and third, it determines whether Millennials and Generation Z differ in these relationships.
Design/methodology/approach
A survey was carried out of Millennial and Generation Z travellers who shared food experiences on social media. Structural equation modelling (SEM) and multi-group analysis were performed to examine the cause-and-effect relationship in both generations.
Findings
The findings reveal differences in motivation, satisfaction, sharing intention, sharing behaviour and loyalty between generations (Millennials and Generation Z).
Research limitations/implications
This study contributes to the literature on the antecedents of food-sharing behaviour in online communities by indicating factors that influence the sharing of culinary experiences and brand or destination loyalty across generations. Suggestions for future research include exploring online food-sharing behaviour through cross-cultural comparisons in various regions.
Practical implications
As Millennials and Generation Z will expand their market share in the coming years, the findings of this study can help improve marketing strategies for culinary tourism and generate more intense food experiences for both generations.
Originality/value
The outcome of the research provides new insights to develop a conceptual model of food-sharing behaviour and tourism on social media by drawing comparisons across generations.
Details
Keywords
The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that…
Abstract
Purpose
The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.
Design/methodology/approach
Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.
Findings
The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.
Research limitations/implications
The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.
Originality/value
The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.
Details
Keywords
Ana Isabel Lopes, Edward C. Malthouse, Nathalie Dens and Patrick De Pelsmacker
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Practical implications
These findings help managers optimize their webcare strategy for better business results and develop automated webcare.
Originality/value
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.
Details