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Natural disasters represent an increasing threat to businesses, putting at risk their continuity in light of sustainable performance conditions. The present chapter explores the role of organizational resilience and of human capital in manufacturing companies hit by a natural disaster, an earthquake in the current study, by considering performance in the long run. In doing so, a survey has been performed on a sample of 131 manufacturing companies hit by the Emilia earthquake (Italy) in 2012, considering both perceptual data and balance sheet data. This represents a key contribution of this chapter, as extant literature on the impact of resilience on business performance has mainly used perceptual data; conversely, our study, considering balance sheet data, enables a more comprehensive and realistic view of the phenomenon. The sample was selected from the AIDA database, as it includes revenue data that we could add to the perceptual measures obtained by administering a structured questionnaire. Partial least squares structural equation modeling (PLS-SEM) was then employed. The results show the importance of developing adaptive processes that leverage on the organization’s human capital and resilience to respond to adverse exogenous events. More specifically, it has been found that human capital and organizational resilience are profitable to post-disaster economic performance in the long run, supporting the economic sustainability of affected businesses. The implications are related to reinforcing new business solutions and adaptive strategies, looking at both organizational resilience and human capital investment to reach a stable economic business performance in the long-run after a detrimental event.

The construction of megaprojects often involves substantial risks. While insurance plays an important role as a traditional risk transfer means, owners and insurance companies may still suffer huge losses during the risk management process. Therefore, considering the strong motivation of insurance companies to participate in the on-site risk management of megaprojects, this study aims to propose a collaborative incentive mechanism involving insurance companies, to optimize the risk management effect and reduce the risk of accidents in megaprojects.

Based on principal-agent theory, the research develops the static and dynamic incentive models for risk management in megaprojects, involving both the owner and insurance company. The study examines the primary factors influencing incentive efficiency. The results are numerically simulated with a validation case. Finally, the impact of parameter changes on the stakeholders' benefits is analyzed.

The results indicate that the dynamic incentive model is available to the achievement of a flexible mechanism to ensure the benefits of contractors while protecting the benefits of the owner and insurance company. Adjusting the incentive coefficients for owners and insurance companies within a specified range promotes the growth of benefits for all parties involved. The management cost and economic benefit allocation coefficients have a positive effect on the adjustment range of the incentive coefficient, which helps implement a more flexible dynamic incentive mechanism to motivate contractors to carry out risk management to reduce risk losses.

This study makes up for the absence of important stakeholders in risk management. Different from traditional megaproject risk management, this model uses insurance companies as bridges to break the island effect of risk management among multiple megaprojects. This study contributes to the body of knowledge by designing appropriate dynamic incentive mechanisms in megaproject risk management through insurance company participation, and provides practical implications to both owner and insurance company on incentive contract making, thus achieving better risk governance of megaprojects.

Assessing inputs and outputs is a significant aspect of taking decisions while there are complex and multistage processes in many examinations. Due to the presence of interval performance measures in various real-world studies, the purpose of this study is to address the changes of interval inputs of two-stage processes for the perturbations of interval outputs of two-stage systems, given that the overall efficiency scores are maintained.

Actually, an interval inverse two-stage data envelopment analysis (DEA) model is proposed to plan resources. To illustrate, an interval two-stage network DEA model with external interval inputs and outputs and also its inverse problem are suggested to estimate the upper and lower bounds of the entire efficiency and the stages efficiency along with the variations of interval inputs.

An example from the literature and a real case study of the banking industry are applied to demonstrate the introduced approach. The results show the proposed approach is suitable to estimate the resources of two-stage systems when interval measures are presented.

To the best of the authors’ knowledge, there is no study to estimate the fluctuation of imprecise inputs related to network structures for the changes of imprecise outputs while the interval efficiency of network processes is maintained. Accordingly, this paper considers the resource planning problem when there are imprecise and interval measures in two-stage networks.

Through its advanced computational capabilities, cyber–physical systems (CPS) proffer solutions to some of the cultural challenges plaguing the effective delivery of facilities management (FM) mandates. This study aims to explore the drivers for the uptake of CPS for FM functions using a qualitative approach – the Delphi technique.

Using the Delphi technique, the study selected experts through a well-defined process entailing a pre-determined set of criteria. The experts gave their opinions in two iterations which were subjected to statistical analyses such as the measure of central tendency and interquartile deviation in ascertaining consensus among the experts and the Mann–Whitney U test in establishing if there is a difference in the opinions given by the experts.

The study’s findings show that six of the identified drivers of the uptake of CPS for FM were attributed to be of very high significance, while 12 were of high significance. Furthermore, it was revealed that there is no significant statistical difference in the opinions given by experts in professional practice and academia.

The study’s outcome provides the requisite insight into the propelling measures for the uptake of CPS for FM by organisations and, by extension, aiding digital transformation for effective FM delivery.

To the best of the authors’ knowledge, evidence from the literature suggests that no study has showcased the drivers of the incorporation of CPS for FM. Hence, this study fills this gap in knowledge by unravelling the significant propelling measures of the integration of CPS for FM functions.

This study proposes a machine learning framework to predict customer complaints from production line tests in an automotive company's lot-release process, enhancing Quality 4.0. It aims to design and implement the framework, compare different machine learning (ML) models and evaluate a non-sampling threshold-moving approach for adjusting prediction capabilities based on product requirements.

This study applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) and four ML models to predict customer complaints from automotive production tests. It employs cost-sensitive and threshold-moving techniques to address data imbalance, with the F1-Score and Matthews correlation coefficient assessing model performance.

The framework effectively predicts customer complaint-related tests. XGBoost outperformed the other models with an F1-Score of 72.4% and a Matthews correlation coefficient of 75%. It improves the lot-release process and cost efficiency over heuristic methods.

The framework has been tested on real-world data and shows promising results in improving lot-release decisions and reducing complaints and costs. It enables companies to adjust predictive models by changing only the threshold, eliminating the need for retraining.

To the best of our knowledge, there is limited literature on using ML to predict customer complaints for the lot-release process in an automotive company. Our proposed framework integrates ML with a non-sampling approach, demonstrating its effectiveness in predicting complaints and reducing costs, fostering Quality 4.0.

The purpose of this study is to investigate the effects of sport scandals on consumer perceptions of the associated sponsors and sport and to provide a typology of sport celebrity scandals to guide management response tactics.

The study conducted four focus groups that were followed by social media data mining. A total of 8,289 consumer comments were collected from 147 websites, and a total of 224 comments were analyzed in terms of themes and frequency.

The research found the impact of sport scandals on consumer perceptions of sponsorship evaluations depended on whether the scandal was gender related scandal, recreational drug use, gender violence, unplanned and planned on-field scandals. Gender violence and planned on-field scandals can have an overwhelmingly negative impact on sponsorship evaluations, while unplanned on-field scandals may result in positive effects. Consumer empathy may influence the impact of recreational drug use, and the gender of the sport celebrity can influence the impact of unplanned on-field scandals.

This study contributes to sponsorship theory by indicating the type of scandal affects consumer perceptions of associated sponsors and sport.

The findings may guide management to develop response tactics to sport scandals. The response tactics may be based on consumer perceptions of the impact of the scandal on the associated sponsors and sport. Sponsor and sport management response tactics may be perceived as a differentiation of the sponsor and sport brands. It may be necessary that sponsorship agreements included pre-determined response tactics that contribute to value formation in the local community.

This study contributes to sponsorship theory by indicating the type of scandal affects consumer perceptions of sponsorship evaluations. Two additional factors may impact these influences: consumer empathy and the gender of the sport celebrity.

The increased utilization of artificial intelligence-enabled applications (AI-ETs) across the customer journey has transformed customer experience (CX), introducing entirely new forms of the concept. This paper aims to explore existing academic research on the AI-enabled customer experience (AICX), identifying gaps in literature and opportunities for future research in this domain.

A systematic literature review (SLR) was conducted in March 2022. Using 16 different keyword combinations, literature search was carried across five databases, where 98 articles were included and analysed. Descriptive analysis that made use of the Theory, Characteristics, Context, Methods (TCCM) framework was followed by content analysis.

This study provides an overview of available literature on the AICX, develops a typology for classifying the identified AI-ETs, identifies gaps in literature and puts forward opportunities for future research under five key emerging themes: definition and dynamics; implementation; outcomes and measurement; consumer perspectives; and contextual lenses.

This study establishes a fresh perspective on the interplay between AI and CX, introducing the AICX as a novel form of the experience construct. It also presents the AI-ETs as an integrated and holistic unit capturing the full range of AI technologies. Remarkably, it represents a pioneering review exclusively concentrating on the customer-facing dimension of AI applications.

随着人工智能应用程序 (AI-ET)在旅途中的使用不断增加, 消费者体验 (CX)得以转变, 引入了全新的概念形式。 本文旨在探索有关人工智能客户体验（AICX）的现有学术研究, 从中找出文献中的空白以及该领域未来研究的机会。

本系统性文献综述（SLR）于2022 年 3 月开工。基于16 个不同的关键词组合, 本综述统共收录并分析了来自 5 个数据库98 篇文献, 采用理论-特征-背景-方法 (TCCM) 框架先后进行描述性分析和内容分析。

该研究概述了 AICX 的现有文献, 开发了对已识别的 AI-ET 进行分类的类型学, 确定了现有文献中的空白, 并在 5 个关键新兴主题下提出了未来研究的机会：1. 定义和动态, 2 . 实施, 3. 结果和衡量, 4. 消费者视角, 5. 情境视角。

本研究建立了全新的视角看待 AI 和 CX 之间的相互作用, 引入了 AICX 这种新颖的体验构造形式, 还将 AI-ET 展示为一个集成了全方位人工智能技术的整体单元。 值得一提的是, 本文代表了一项专门关注人工智能应用面向客户维度的开创性综述。

La creciente utilización de aplicaciones habilitadas por inteligencia artificial (AI-ET) a lo largo del recorrido del cliente han transformado la experiencia del cliente (CX), introduciendo formas totalmente nuevas del concepto. Este artículo pretende explorar la investigación académica existente sobre la experiencia del cliente a través de la IA (AICX), identificando las lagunas en la literatura y las oportunidades para futuras investigaciones en este ámbito.

En marzo de 2022 se llevó a cabo una revisión bibliográfica sistemática (SLR). Utilizando 16 combinaciones diferentes de palabras clave, se realizó una búsqueda bibliográfica en 5 bases de datos en las que se incluyeron y analizaron 98 artículos. El análisis descriptivo que hizo uso del marco Teoría, Características, Contexto, Métodos (TCCM) fue seguido del análisis de contenido.

El estudio ofrece una visión general de la bibliografía disponible sobre la AICX, desarrolla una tipología para clasificar las AICX detectadas, identifica lagunas en la literatura y plantea oportunidades para futuras investigaciones bajo cinco temas emergentes claves: 1. Definición y dinámica, 2. Implementación, 3. Resultados y medición, 4. Perspectivas del consumidor, 5. Lentes contextuales.

El estudio establece una nueva perspectiva sobre la interacción entre la IA y la CX, introduciendo la AICX como una forma novedosa del constructo experiencia. También presenta las AICX como una unidad integrada y holística que capta toda la gama de tecnologías de la IA. Notablemente, representa una revisión pionera que se concentra exclusivamente en la dimensión orientada al cliente de las aplicaciones de la IA.

In the accounting field, sustainability accounting (SA) has evolved as a valuable tool that links improvements in environmental, social and governance issues to financial performance. This study aims to examine the structure and evolution of SA research, map the state of knowledge and analyse the literature trends and gaps.

The study adopts a bibliometric review technique with data sourced from the Scopus database. A total of 7,049 extant literature spanning from 1982 to 2022 was analysed using the VOSviewer software.

The authors find a significant growth in the number of publications on SA research, primarily driven by collaboration among researchers from Europe and America. The analysis highlights emerging themes, structure and discusses in detail the changing phases of SA research over the past four decades while highlighting key events that have impacted the development of SA research. Furthermore, the dominant theories used by extant studies are discussed and potential avenues for future research are provided. The authors draw the attention of the research community to the dominant authors, the most cited articles, prominent publication outlets and countries advancing research in this field.

This study advances knowledge on SA research by providing a retrospective assessment of the state of knowledge in the field while highlighting avenues for future research.