Search results

This study aims to empirically test how sponsored video customization (i.e. the degree to which a sponsored video is customized for a sponsoring brand) affects video shares differently depending on influencer characteristics (i.e. mega influencer and expert influencer) and brand characteristics (i.e. brand establishment and product involvement).

This study uses a unique real-world data set that combines coded variables (e.g. customization) and objective video performance (e.g. sharing) of 365 sponsored videos to test the hypotheses. A negative binomial model is used to analyze the data set.

This study finds that the effect of video customization on video shares varies across contexts. Video customization positively affects shares if they are made for well-established brands and high-involvement products but negatively influences shares if they are produced by mega and expert influencers.

This study extends the influencer marketing literature by focusing on a new media modality – sponsored video. Drawing on the multiple inference model and the persuasion knowledge theory, this study teases out different conditions under which video customization is more or less likely to foster audience engagement, which both influencers and brands care about. The chosen research setting may limit the generalizability of the findings of this study.

The findings suggest that mega and expert influencers need to consider if their endorsement would backfire on a highly customized video. Brands that aim to engage customers with highly-customized videos should gauge their decision by taking into consideration their years of establishment and product involvement. For video-sharing platforms, especially those that are planning to expand their businesses to include “matching-making services” for brands and influencers, the findings provide theory-based guidance on optimizing such matches.

This paper fulfills an urgent research need to study how brands and influencers should produce sponsored videos to achieve optimal outcomes.

The tourist industry has to adopt a big data-driven foresight approach to enhance decision-making in a post-COVID international landscape still marked by significant uncertainty and in which some megatrends have the potential to reshape society in the next decades. This paper, considering the opportunity offered by the application of the quantitative analysis on internet new data sources, proposes a prediction method using Google Trends data based on an estimated transfer function model.

The paper uses the time-series methods to model and predict Google Trends data. A transfer function model is used to transform the prediction of Google Trends data into predictions of tourist arrivals. It predicts the United States tourism demand in Italy.

The results highlight the potential expressed by the use of big data-driven foresight approach. Applying a transfer function model on internet search data, timely forecasts of tourism flows are obtained. The two scenarios emerged can be used in tourism stakeholders’ decision-making process. In a future perspective, the methodological path could be applied to other tourism origin markets, to other internet search engine or other socioeconomic and environmental contexts.

The study raises awareness of foresight literacy in the tourism sector. Secondly, it complements the research on tourism demand forecasting by evaluating the performance of quantitative forecasting techniques on new data sources. Thirdly, it is the first paper that makes the United States arrival predictions in Italy. Finally, the findings provide immediate valuable information to tourism stakeholders that could be used to make decisions.

The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

Given the significance of innovation in enabling firms to maintain a long-term competitive edge and secure excess profits, this paper aims to investigate whether and how stakeholders’ attention to innovation (SATI) influences corporate innovation.

This paper introduces a novel variable, SATI, which is achieved by segmenting stakeholders’ attention into two categories: attention to innovation and attention to other facets, using textual analysis methods. Subsequently, this paper empirically examines the influence of SATI on corporate innovation.

This paper finds that SATI positively affects corporate innovation input, and the result remains true after addressing possible endogeneity issues using instrumental variable regression. Furthermore, the positive effect of SATI on corporate innovation is stronger in firms facing greater financing constraints, thus verifying the financing constraints hypothesis. The positive effect is also stronger in firms with lower risk-taking levels, thus confirming the innovation failure tolerance hypothesis. Further analysis suggests that SATI increases both corporate innovation output and efficiency, thus ruling out the catering hypothesis.

This paper highlights the importance of SATI in driving corporate innovation. It enriches the literature on the repercussions of stakeholders’ attention and determinants of corporate innovation. In addition, it provides practical suggestions for further implementing China’s national innovation-driven development strategy.

Despite extensive academic research related to digital technologies (DT), their integration into architecture, engineering and construction (AEC) projects lags in practice. This paper aims to discover DT deployment patterns and emerging trends in real-life AEC projects.

A case study methodology was adopted, including individual case analyses and comparative multiple-case analyses.

The results revealed the temporal distribution of DT in practical AEC projects, specific DT products/software, major project types integrated with digital solutions, DT application areas and project stages and associated project performance. Three distinct patterns in DT adoption have been observed, reflecting the evolution of DT applications, the progression from single to multiple DT integration and alignment with emerging industry requirements. The DT adoption behavior in the studied cases has been examined using the technology-organization-environment-human (TOE + H) framework. Further, eight emerging trend streams for future DT adoption were identified, with “leveraging the diverse features of certain mature DT” being a shared recognition of all studied companies.

This research offers actionable insights for AEC companies, facilitating the development of customized DT implementation roadmaps aligned with organizational needs. Policymakers, industry associations and DT suppliers may leverage these findings for informed decision-making, collaborative educational initiatives and product/service customization.

This research provides empirical evidence of applicable products/software, application areas and project performance. The examination of the TOE + H framework offers a holistic understanding of the collective influences on DT adoption. The identification of emerging trends addresses the evolving demands of the AEC industry in the digital era.

Corona Virus Disease (Covid-19) is a global pandemic that emerged at the end of 2019 and caused disruptions in global supply chains, particularly in the food supply chains that exposed the vulnerability of today’s food supply chain in a major disruption which provided a unique research opportunity. This review explores the current research direction for food supply chain resilience and identifies gaps for future research in preparing for future major global pandemics.

This article presents a review of food supply chain resilience followed a systematic literature review of the business and management-based studies related to the food supply chain in Covid-19 published between December 2019 and December 2021 to identify the immediate issues and responses that need to be addressed in the event of future disruptions in food supply chains due to new global health threats.

The study revealed the need for more literature on food supply chain resilience, particularly resilience to a major global pandemic. The study also uncovered the sequence of events in a major pandemic and identified some strategies for building resilience to potential future risks of such an event.

The limitations of this study are apparent. Firstly, the selection of databases is not comprehensive. Due to time limitations, authoritative publishers such as Springer, Emerald, Wiley and Taylor & Francis were not selected. Secondly, a single author completed the literature quality testing and text analysis, possibly reducing the credibility of the results due to subjective bias. Thirdly, the selected literature are the studies published during the immediate event of Covid-19, and before January 2022, other research studies may have been completed but were still in the state of auditing at this time.

This paper is the first study that provides a detailed classification of the immediate challenges to the food supply chain faced in both upstream and downstream nodes during a major global disruption. For researchers, this clearly shows the immediate difficulties faced at each node of the food supply chain, which provides research topics for future studies.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

The link between interlocking directors and mergers and acquisitions (M&A) efficiency has been analyzed in an information asymmetry environment. Despite an abundance of evidence highlighting that interlocking directors do contribute to M&A efficiency in an acquirer-target binary relationship, the target is embedded in a complex network of supplier-customer relationships, which implies that the acquirer needs to consider the value of suppliers, distributors and retailers in the target’s supply chain in improving M&A efficiency. Through the lenses of acquirer-target multivariate relationships, this paper aims to examine how directors with supply chain experience (DSCs) act as heterogeneous network pipes to affect M&A efficiency.

Using a sample of 311 A-share listed firms on the Shanghai and Shenzhen stock exchanges in China during 2011–2020, this paper investigates the relationship between DSCs and M&A efficiency by using ordinary least squares (OLS) regression.

Through empirical research, we verify a negative relationship between DSCs and M&A duration and an inverted U-shaped relationship between both DSCs and M&A performance, revealing the complexity of the relationship between experience and efficiency. Furthermore, drawing on upper echelon theory, the information value of DSCs will be greatly reduced when executives have overconfident psychological characteristics, which are mainly shown to negatively moderate the relationship between DSCs and M&A performance. We also conduct multiple robustness tests and supplemental analyses to illustrate the robustness and boundaries of our findings. Finally, DSCs are likely more important in environments among growth and mature firms as well as high-growth industries.

We break through the assumption that interlocking directors contribute to M&A efficiency in an acquirer-target binary relationship and examine the impact of DSCs on M&A efficiency based on micro-empirical evidence from the value of target-related upstream or downstream industries, which extends the connotation of interlocking directors and enriches the study related to factors influencing M&A efficiency.