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The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

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.

This paper aims to reveal the influence of the presentation of online product information (POPI) on consumer attitudes in the context of online buying digital products.

Two main experimental designs are used to collect data. The ANOVA, t-test and Bootstrap methods are applied to check hypotheses.

Findings of Study 1 indicate that if the POPI is combined with different types of celebrity endorsement (CE) (real vs virtual), the self-brand connection will be changed and further influence consumer attitudes toward digital products. Study 2 verifies the diverse moderating effects of the type of virtual CE. The CRP (central-route presentation) online product information with SVCE (super-realistic-digital virtual CE) can decrease consumer attitudes, while the PRP (peripheral-route presentation) online product information with AVCE (anthropomorphic virtual CE) can enhance consumer attitudes.

E-commerce enterprises should optimize the current layout of POPI by considering diverse matchings between POPI and CE to increase consumer attitudes. Moreover, marketers could make various schemes of POPI considering (virtual) CE and self-brand connection.

Findings contribute to understanding the relationship between POPI and consumer attitudes considering the mediation of self-brand connection and the mediations of virtual/real CE. Additionally, this study bridges the gap between research on virtual CE and business practices.

As emerging scholars of color with transnational backgrounds, we collectively recount our socialization experiences in US higher education institutes. We explore moments of betweenness as catalysts for envisioning a more inclusive academia that operates beyond the tokenism of diversity.

Employing betweener autoethnography (Diversi and Moreira, 2018), we inquire into the sense of impasse encountered by South Korean female emerging scholars in the field of education in becoming an outsider within the academic system.

Chronicling our shifts in perspectives of our positionality, we interweave inquiries motivating us to challenge normative pressures and map our betweener experiences onto the Wiedman and DeAngelo’s (2020) socialization model. Through this process, we wedge open in-between spaces in the socialization process that accommodate the nuanced positionality of transnational scholars.

Integrating postcolonial critiques on the Western-centric meritocratic academia, this piece sheds light on the complexity and fluidity of emerging transnational scholars’ socialization processes. The thick, nuanced description deepens the understanding of the complexity of their identity negotiation within the dominant logics of academia. Our inquiries interwoven through betweener autoethnography serve as guidance for mentoring international graduate students and transnational scholars.

Research on the relationship between customer bargaining power and supplier performance in supplier–customer relationships has flourished in recent decades. This study aims to empirically investigate whether product market overlap (PMO) in a supply chain moderates the effect of customer bargaining power on supplier profitability.

This study uses large-scale secondary data from multiple databases. Econometric panel data techniques are used to test the hypotheses.

The results show that PMO in a supplier–customer relationship and PMO in supplier–supplier relationships both exacerbate the negative effect of the bargaining power of customers on supplier profitability.

This study contributes to the field of supply chain management. This study brings new insights into the ongoing debate surrounding the relationship between customer bargaining power and supplier profitability. The study also contributes to the literature on supply chain networks by showing the impact of indirect supply chain relationships.

The purpose of the study is to investigate the impact of asset redeployability on the level of corporate cash holdings.

The authors use regression analysis to examine the relation between asset redeployability and corporate cash holdings.

Using a large panel sample of US public firms from 1990 to 2020, the authors find a significant positive relation between asset redeployability and cash, which suggests that firms with more redeployable assets hold more cash.

The authors contribute to a growing literature in accounting and finance that investigates the impact of asset redeployability on firm characteristics and also contribute to the literature on the determinants of cash holdings.

Conventional wisdom suggests that war in the host country makes it unattractive for foreign firms to invest. To see if this is true for US firms on the aggregate, this paper aims to examine the veracity of a “permanent war economy” hypothesis, that foreign direct investment (FDI) may, in fact, increase in the host country not despite, but because of, war, i.e. one that lends credence to the idea that, in the USA, “defense [has] become one of constant preparation for future wars and foreign interventions rather than an exercise in response to one-off threats.”

The authors test the hypotheses using Generalized Method of Moments estimation, with Heckman Selection, on US FDI data from the Bureau of Economic Analysis and war data from the Correlates of War2 Project, the Uppsala Conflict Data Program/International Peace Research Institute data set, the International Crisis Behavior Project and the Center for Systemic Peace Major Episodes of Political Violence data set. The final sample consists of 351 country-year observations in 55 host countries from 1982 to 2006.

The findings indicate that overall US FDI in a host country in a given year decreases if the host country is engaged in wars with multiple countries and if the US Government is involved in the war. Most notably, the results show that US involvement in multiple host country wars is actually correlated with increased US FDI into the host country, providing empirical support for the “permanent war economy” hypothesis.

While other studies have focused on war and FDI, the authors have sought to show the impact of the involvement of arguably the most influential country, i.e. the USA, in the sovereign matters of a focal host country. By studying FDI from the USA as a function of US involvement in wars overseas, over the years with the greatest use of private military companies by the USA and the largest portion of global FDI accounted for by the USA, this work motivates a research agenda on home-host-"other” relations in the context of war and FDI, with the “other” being the supranational “elephant in the room.”

This paper aims to propose and assess a framework to analyse the institutionalization of Industry 4.0 (I4.0) through a framing analysis.

The framework was developed by combining the institutional approach with orders of worth, drawing insights from a comprehensive literature review. To assess it, the authors conducted a qualitative analysis of annual reports from companies with the largest market capitalization over a six-year period and interviewed union representatives in Spain and Sweden.

The framework comprises five dimensions (industrial, market, civic, green and connectionist). The empirical results reveal that companies consistently frame I4.0 with an emphasis on industrial and market perspectives. In contrast, unions place a stronger emphasis on civic issues, with Spanish unions holding a more negative view of I4.0, expressing concerns about working conditions and unemployment.

The proposed framework brings interesting insights into the dispute over the meaning of I4.0. Although this empirical study was limited to companies and unions in Sweden and Spain, the framework can be expanded for broader investigations, involving additional stakeholders in one or more countries. The discussion outlined using the varieties of capitalism approach is relevant for understanding the connection between the meso and macro levels of this phenomenon.

In navigating the landscape of I4.0, managers should remain flexible, and ready to tailor their strategies and operations to align with the distinct demands and expectations of stakeholders and their specific institutional environments. Similarly, policymakers are urged to acknowledge these contextual intricacies when crafting strategies for implementing I4.0 initiatives across national settings.

Based on the empirical findings, this study underscores the importance of fostering social dialogue and involving stakeholders in the implementation of I4.0. Policymakers and other stakeholders should take proactive measures, tailored to each country’s context, to mitigate potential adverse effects on labour and workers.

The study presents a novel framework that facilitates the systematic comparison of I4.0 framing by different actors. This contribution is significant because the way actors frame I4.0 affects its interpretation and implementation. Additionally, the aggregate analysis of results enables cross-country comparisons, enhancing our understanding of regional disparities.

This study aims to examine how the knowledge breadth and depth of a patent affect its likelihood of being pledged. It also seeks to explore whether these relationships change diversely in different technological environments.

A complementary log-log model with random effects was conducted to test the hypotheses using a unique data set consisting of 348,927 invention patents granted by the China National Intellectual Property Administration from 1985 to 2015 belonging to 74,996 firms.

The findings reveal that both knowledge breadth and depth of a patent positively affect its likelihood of being pledged. Furthermore, the knowledge breadth and depth entail different degrees of superiority in different technological environments.

This study focuses on the effect of an individual patent’s knowledge base on its likelihood of being selected as collateral. It does not consider the influence of the overall knowledge characteristics of the selected patent portfolio.

Managers need to pay attention to patents’ knowledge characteristics and the changes in technological environments to select the most suitable patents as collateral and thus improve the success rate of pledge financing.

This study explores the impact of multidimensional characteristics of knowledge base on patent pledge financing within a systematic theoretical framework and incorporates technological environments into this framework.

Radical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination.

This study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables.

The results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism.

This study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.

2019冠狀病毒病引致消費者習慣有根本的改變; 這些改變顯示，根據歷史序列而運作的慣常需求預測技巧未必是正確的。這不確性尤以受到大流行極大影響的酒店服務需求為甚。因此，我們擬探討、若把在推特網站上的旅遊活動視為聖雅各之路 (一個重要的朝聖旅遊聖地) 酒店服務需求的預測器，這會否是合適的呢?

本研究比較 SARIMA 時間序列模型與附有外生變數 (SARIMAX)模型兩者在預測旅遊及酒店服務需求方面的表現。為此，研究人員收集在推特網站上發佈的資訊，作為外生變數進行研究。這個樣本涵蓋於2018年1月至2022年9月期間110,456個發佈資訊。

研究結果確認了傳統的時間序列模型，若涵蓋推特網站上的旅遊活動，則其對旅遊需求方面的預測會得到顯著的改善。推特網站的數據，就改善預測實時旅遊需求的準確度，或許可成為有效的工具; 而這發現對旅遊管理會有一定的意義。本研究亦讓我們進一步瞭解朝聖旅遊方面旅客的數碼足跡。

現存文獻甚少探討朝聖旅遊的數字化，而本研究不但在這方面充實了有關的文獻，還使用了一個根據推特網站上使用者原創內容嶄新的方法框架，進行分析和探討。這會幫助酒店從業人員把社交媒體數據轉變為可供酒店管理之用的合宜資訊。