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A daily vlog is a new type of film tourism that has been underexplored in the literature. This study aims to explore the key elements of daily vlogs that trigger a followers’ travel intention within the framework of enduring involvement. The study will highlight the affect-inspiring role of para-social relationships (PSR), extending the scope of film tourism research.

In Study 1, the objective was to investigate the essential components of enduring involvement in destination-life scenarios. This was accomplished through the analysis of qualitative data, including interview data collected from 15 participants and more than 20,000 words extracted from online blogs, barrages and comments. Additionally, Study 2 used 376 questionnaires to test the proposed structural model.

Within daily vlog-induced tourism, PSR assumes a pivotal role as a catalyst for evoking emotions. This phenomenon is interwoven with enduring involvement and has a direct impact on the determination of travel intention. Within the contextual framework of enduring involvement, constituents such as destination-life self-disclosure, situational resonance and affective belonging converge synergistically. These elements collectively underpin the process through which followers harness PSR as a driving force for nurturing travel intentions within the realm of daily vlog-induced tourism.

This study proposes the concept of daily vlog-induced tourism and emphasizes the emotion-inspiring role of PSR, to extend film tourism research to include daily vlogs. The study then explores the influencing factors of daily vlog-induced tourism under the framework of enduring involvement, providing a new understanding of enduring involvement in daily vlog-induced tourism.

日常视频博客是一种新型的电影旅游, 但文献中对它的研究还不够。本研究旨在持久卷入的框架内探讨日常视频博客触发追随者旅游意向的关键因素。本研究将强调辅助准社会关系的情感激励作用, 从而扩展电影旅游研究的范围。

研究1的目的是调查目的地生活场景中持久参与的基本要素。为此, 研究人员分析了定性数据, 包括从15名参与者那里收集的访谈数据, 以及从在线博客、弹幕（即时更新的互动评论系统）和评论中提取的 20,000 多字的数据。此外, 研究2利用 376 份调查问卷对所提出的结构模型进行了检验。

在由日常博客引发的旅游中, 准社会关系作为唤起情感的催化剂发挥着举足轻重的作用。这一现象与持久参与交织在一起, 并对旅游意向的产生产生直接影响。在持久参与的情境框架内, 目的地生活的自我披露、情境共鸣和情感归属等要素协同作用。这些要素共同支撑着追随者利用准社会关系的过程, 准社会关系在日常视频博客引发的旅游领域中是培育旅游意愿的驱动力。

本研究提出了“日常视频博客诱导旅游” 的概念, 并强调了准社会关系的情感激励作用, 从而将电影旅游研究扩展至日常微博。随后, 研究在持久参与的框架下探讨了日常视频博客诱发旅游的影响因素, 为日常视频博客诱发旅游中的持久参与提供了新的理解。

Los “vlogs” diarios son un nuevo tipo de turismo cinematográfico poco explorado en la bibliografía. Este estudio pretende explorar los elementos clave de los vlogs diarios que desencadenan la intención de viaje de los seguidores en el marco de la implicación duradera. El estudio destacará el papel inspirador de afecto de las relaciones para-sociales (RPS), ampliando el alcance de la investigación sobre el turismo cinematográfico.

En el Estudio 1, el objetivo era investigar los componentes esenciales de la implicación duradera en escenarios de vida en destino. Para ello se analizaron datos cualitativos, incluidos datos de entrevistas realizadas a 15 participantes y más de 20.000 palabras extraídas de blogs, “barrages” (un sistema de comentarios interactivos actualizados al instante) y comentarios. Además, el Estudio 2 utilizó 376 cuestionarios para poner a prueba el modelo estructural propuesto.

Dentro del turismo cotidiano inducido por los “vlogs”, la RSP asume un papel fundamental como catalizador para evocar emociones. Este fenómeno se entrelaza con la implicación duradera y tiene un impacto directo en la determinación de la intención de viaje. En el marco contextual de la implicación duradera convergen sinérgicamente elementos como la autodivulgación de la vida en el destino (AD), la resonancia situacional y la pertenencia afectiva. Estos elementos apuntalan colectivamente el proceso a través del cual los seguidores aprovechan el PSR como fuerza motriz para alimentar las intenciones de viaje en el ámbito del turismo cotidiano inducido por los “vlogs”.

Este estudio propone el concepto de turismo diario inducido por “vlogs” y hace hincapié en el papel inspirador de emociones del PSR, para ampliar la investigación sobre el turismo cinematográfico e incluir los “vlogs” diarios. A continuación, el estudio explora los factores que influyen en el turismo diario inducido por “vlogs” bajo el marco de la implicación duradera, proporcionando una nueva comprensión de la implicación duradera en el turismo diario inducido por “vlogs”.

High-quality cold-chain logistics are key to effectively managing the quality of temperature-sensitive foods. Hence, this study investigates the service quality of such logistics, using a real-life case of temperature-sensitive milk delivery.

This study focuses on developing business analytics for quality control in cold-chain perishable-food logistics, grounded in normal accident theory and stakeholder theory, and tests them using real-world data.

The empirical business-analytics results indicate that cargo locations, logistics status and delivery times are the essential factors that influence the quality of temperature-sensitive milk.

This study confirms that a combination of normal accident theory and stakeholder theory can be usefully applied to the development of strategies for managing perishable-food logistics. As such, its proposed business analytics provide a fresh foundation for research on logistics quality management.

The balance between efficiency and service quality in a logistics system should be assessed carefully, and logistics companies should collaborate with their stakeholders and can help to mitigate potential cold-chain risks.

This pioneering analytical study explores the essential quality issues that confront cold chains and demonstrates how to extract vital insights from temperature-sensitive food logistics monitoring data. As such, it represents a noteworthy contribution to the field of logistics quality management.

Cargo loss has been a major issue in logistics management. However, few studies have tackled the issue of cargo loss severity via business analytics. Hence, the purpose of this paper is to provide guidance about how to retrieve valuable information from logistics data and to develop cargo loss mitigation strategies for logistics risk management.

This study proposes a research design of business analytics to scrutinize the causes of cargo loss severity.

The empirical results of the decision tree analytics reveal that transit types, product categories, and shipping destinations are key factors behind cargo loss severity. Furthermore, strategies for cargo loss prevention were developed.

The proposed framework of cargo loss analytics provides a research foundation for logistics risk management.

Companies with logistics data can utilize the proposed business analytics to identify cargo loss factors, while companies without logistics data can employ the proposed cargo loss mitigation strategies in their logistics systems.

This pioneer empirical study scrutinizes the critical cargo loss issues of cargo damage, cargo theft, and cargo liability insurance through exploiting real cargo loss data.

In the reduction of food waste and the provision of food to the hungry, food banks play critical roles. However, as they are generally run by charitable organisations that are chronically short of human and other resources, their inbound logistics efforts commonly experience difficulties in two key areas: 1) how to organise stocks of donated food, and 2) how to assess the donated items quality and fitness for purpose. To address both these problems, the authors aimed to develop a novel artificial intelligence (AI)-based approach to food quality and warehousing management in food banks.

For diagnosing the quality of donated food items, the authors designed a convolutional neural network (CNN); and to ascertain how best to arrange such items within food banks' available space, reinforcement learning was used.

Testing of the proposed innovative CNN demonstrated its ability to provide consistent, accurate assessments of the quality of five species of donated fruit. The reinforcement-learning approach, as well as being capable of devising effective storage schemes for donated food, required fewer computational resources that some other approaches that have been proposed.

Viewed through the lens of expectation-confirmation theory, which the authors found useful as a framework for research of this kind, the proposed AI-based inbound-logistics techniques exceeded normal expectations and achieved positive disconfirmation.

As well as enabling machines to learn how inbound logistics are handed by human operators, this pioneering study showed that such machines could achieve excellent performance: i.e., that the consistency provided by AI operations could in future dramatically enhance such logistics' quality, in the specific case of food banks.

This paper’s AI-based inbound-logistics approach differs considerably from others, and was found able to effectively manage both food-quality assessments and food-storage decisions more rapidly than its counterparts.

The study explored users' tendency of confirmation bias when processing congenial vs. uncongenial electronic-word-of-mouth (e-WOM) about mystery fictions, a hedonic product category with strong experience and hedonic characters.

A two-stage judgment approach was employed where the participants were asked to judge a set of mystery novels twice: one before, and another after they were exposed to positive and negative e-WOM. The first-stage judgment established two favored and two disfavored titles by each participant. They were then asked to read six consumer reviews – three positive and three negative – for each of the four titles. The procedures created four review evaluation situations: two congruent and two incongruent, which allowed the authors to assess the participants' perceptions of congenial and uncongenial reviews and their rating adjustments of the titles. Participants' involvement in mystery novels was also measured to test its moderating effect on confirmation bias.

Confirmation bias in the evaluation of e-WOM was observed and reinforced by the user's involvement in the genre. Congenial reviews were perceived to be significantly more credible, better reflect the intrinsic value of a title and less subjectively motivated than uncongenial reviews. Furthermore, after exposure to equal amount of positive and negative e-WOM, an asymmetrical adjustment of final rating of the titles was observed. A significantly greater downward adjustment was observed for disfavored than favored titles. Stronger positive confirmation bias was also observed in the evaluation of WOM.

Previous studies on e-WOM have shown conflicting findings on the relative efficacy of positive vs. negative reviews. By introducing the factor of prior attitudes, the study demonstrated that whether WOM is consistent with an individual's prior attitude, rather than the valences of WOM in itself, determines its persuasiveness. Thus, it established the confirmation bias in users' processing of e-WOM. The finding highlights the importance for marketers to establish a positive initial impression, which, as the findings demonstrated, helps alleviate the damages caused by negative WOM.

This is the first study that has ever attempted to study the effect of confirmation bias during the users' processing of e-WOM in an experimental setting. By having the participants judge the books before and after exposure to congenial and uncongenial e-WOM, the authors were able to establish the link between the users' prior commitment to a book and their subsequent judgment of both the titles and the e-WOM.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-01-2020-0026

The purpose of this paper is to apply expectation-confirmation theory (ECT) and the unified theory of acceptance and use of technology (UTAUT) to identify potential determinants of library patrons’ intention to continue using (continuance) self-issue and return systems for borrowing and returning books. The role of continuance is specifically interesting and is well-established in the literature of information systems (ISs) and marketing because continuance is considered to be the central element for the survival of ISs and of self-service technology (SST). However, the role of continuance has been neglected by researchers studying library SST adoption.

A research model integrating the expectation-confirmation model of IS continuance with UTAUT was developed and empirically validated using data obtained from a field survey involving 128 active users of a self-issue and return system at a public library in Taiwan.

Library patrons’ continuance intention was affected by their post-adoption expectation of perceived performance, effort performance and facilitating conditions as well as their satisfaction when using self-issue and return systems. Moreover, a patron satisfaction level was influenced by performance expectancy and confirmation of initial expectations, whereas confirmation had a positive effect on all post-adoption expectations.

This study broadens perspectives on SST adoption behavior in the library context by identifying some factors that influence library patrons’ continuance decisions. Furthermore, the authors verified the applicability and predictability of the theoretical integration of ECT with UTAUT in a diverse setting because the authors sought survey participants outside of the college population.

Delivery vulnerability is a critically important theme in logistics risk management. However, while logistics service providers often collect and retain massive amounts of logistics data, they seldom utilize such information to diagnose recurrent day-to-day logistics risks. Hence, the purpose of this paper is to investigate delivery vulnerabilities in a logistics system using its own accumulated data.

This study utilizes pragmatic business analytics to derive insights on logistics risk management from operations data in a logistics system. Additionally, normal accident theory informs the discussion of its management implications.

This study’s analytical results reveal that a tightly coupled logistics system can align with normal accident theory. Specifically, the vulnerabilities of such a system comprise not only multi-components but also interactive ones.

The tailored business analytics comprise a research foundation for logistics risk management. Additionally, the important research implications of this study’s analytical results arrived at via such results’ integration with normal accident theory demonstrate the value of that theory to logistics risk management.

The trade-offs between logistics risk and logistics-system efficiency should be carefully evaluated. Moreover, improvements to such systems’ internal resilience can help to alleviate potential logistics vulnerabilities.

This pioneering analytical study scrutinizes the critical vulnerability issues of a logistics service provider and therefore represents a valuable contribution to the field of logistics risk management. Moreover, it provides a guide to retrieving valuable insights from existing stockpiles of delivery-vulnerability data.

The purpose of this study was to develop the augmented reality (AR) educational program combined with the instructional guidance for supportive learning, which enhanced the thinking process cooperative discussion and problem-solving skills in chemistry subject.

The method used the quasi-experimental research design. Of the 45 students who attended this experiment, only 25 with low achievement qualified in operating the AR learning system of saponification and transesterification environment (ARLS-STE) system.

These results confirmed that the AR educational program could have increased substantial benefits in improvements of students’ knowledge and the ability of the thinking process for the participants with the lowest score. In semi-structured interviews, most of participants enjoyed manipulating the ARLS-STE system, which was realistic, motived and interesting for learning science subjects.

The low-achieving students have often been known with a low learning capability, and they lack in developing constructional knowledge, despite being keen for learning. Regarding educational concerns for this population, providing orientated learning and supportive materials could increase their learning effects. Virtual worlds are an efficient learning tool in educational setting. The AR can offer visual concepts and physical interaction for students with low achievement in learning. Thus, this study investigates the acceptability of an educational program designed in the ARLS-STE, which involves the learning effects of academic knowledge and the capability of thinking process for students with low achievement. The ARLS-STE system was developed for this proposal, based upon the marker-based AR technologies combined with hands-on manipulation.

In recent years, cost overrun becomes a common problem in steel building construction projects. The average percentage can vary widely depending on the project type, size, complexity and location. The steel structure change ratio in Taiwan is from 1 to 18% in statistics. The contractors always put every possible effort into preventing or mitigating project cost overruns, and one of the approaches is an accurate cost overrun risk estimate. Traditional project cost overrun risk assessment models mainly focus on macro-level evaluation and may not function well for the project-specific level (micro-level). This study creates a network-like connection model between the outcome (i.e. cost overrun risk) and the associated root causes in which the project status evaluation checklists of design, manufacturing, construction and interfaces are used to evaluate the checklists' influences through the Bayesian network (BN) composed by intermediate causes.

Due to the constraint of data availability, BN nodes, relationships and conditional probabilities are defined to establish a BN-based steel building project cost overrun assessment model following the knowledge of experts. Because of the complexity of the BN, the construction of the BN structure is first to build BN's fault tree (FT) hierarchy. And then, basic BN framework is constructed by the transformation of the FT hierarchy. Furthermore, some worthwhile additional arcs among BN nodes are inserted if necessary. Furthermore, conditional probability tables (CPTs) among BN nodes are explored by experts following the concept of the ranked node. Finally, the BN-based model was validated against the final cost analysis reports of 15 steel building projects done in Taiwan and both were highly consistent. The overall BN-based model construction process consists of three steps: (1) FT construction and BN framework transformation, (2) CPT computation and (3) model validation.

This study established a network-like bridge model between the outcome (i.e. cost overrun risk) and the root causes in a network of which cost influences are evaluated through the project-specific status evaluation checklists of design, manufacturing, construction and interfaces. This study overcame several limitations of the previous cost overrun risk assessment models: (1) few past research support assessment of cost overrun based on real-time project-owned data and (2) the traditional causal models inadequately depict interdependencies among influence factors of cost overrun at the network. The main influence factors of the cost overrun risk at the steel building projects in Taiwan were also examined using sensitivity analysis. The main root causes of cost overrun in steel building projects are design management and interface integration.

The proposed model belongs to the project-specific causal assessment model using real-time project-owned status checklist data as input. Such a model was seldom surveyed in the past due to the complicated interdependence among causes in the network. For practical use, a convenient and simple regression equation was also developed to forecast the cost overrun risk of the steel building project based on the root causes as input. Based on the analysis of cost overrun risk and significant influence factors, proper tailor-made preventive strategies are established to reduce the occurrence of cost overrun at the project.

This paper aims to develop a dynamic civil facility degradation prediction model to forecast the reliability performance tendency and remaining useful life under imperfect maintenance based on the inspection records and the maintenance actions.

A real-time hidden Markov chain (HMM) model is proposed in this paper to predict the reliability performance tendency and remaining useful life under imperfect maintenance based on rare failure events. The model assumes a Poisson arrival pattern for facility failure events occurrence. HMM is further adopted to establish the transmission probabilities among stages. Finally, the simulation inference is conducted using Particle filter (PF) to estimate the most probable model parameters. Water seals at the spillway hydraulic gate in a Taiwan's reservoir are used to examine the appropriateness of the approach.

The results of defect probabilities tendency from the real-time HMM model are highly consistent with the real defect trend pattern of civil facilities. The proposed facility degradation prediction model can provide the maintenance division with early warning of potential failure to establish a proper proactive maintenance plan, even under the condition of rare defects.

This model is a new method of civil facility degradation prediction under imperfect maintenance, even with rare failure events. It overcomes several limitations of classical failure pattern prediction approaches and can reliably simulate the occurrence of rare defects under imperfect maintenance and the effect of inspection reliability caused by human error. Based on the degradation trend pattern prediction, effective maintenance management plans can be practically implemented to minimize the frequency of the occurrence and the consequence of civil facility failures.