bluuebunny/crossref_metadata_2025_split huggingface dataset:
Understanding customer’s post-M&A intentions and behaviors: the role of the family business brand and previous reputation of the acquiring firm
Authors: Strano Sonia M. , Botero Isabel C. , Fediuk Tomasz A. , Pisano Vincenzo 
Published: August 2024

Purpose Mergers and acquisitions (M&As) are a critical time for organizations and their consumers. For the company, there are many financial and non-financial risks. For customers, it requires deciding whether or not to continue the relationship that they had with the previous firm. This paper explores the extent to which communicating the family business (FB) brand, and the previous reputation of the acquirer affects customer perceptions and intentions after an M&A event.Design/methodology/approach Data for this study were collected from 159 Italian participants. We used a 2 (Communication of FB brand: Yes vs. No) by 3 (Reputation: positive, neutral, negative) between subjects’ experiment to test how the communication of the FB brand and the reputation of the acquirer affected perceived trustworthiness and service quality, and how this, in turn, influenced customer purchase intentions.Findings We find that communicating the FB brand does not influence consumer perceptions and intentions toward the acquired company. However, the previous reputation of the acquiring firm is critical in influencing consumer perceptions and intentions to buy.Originality/value Our study continues the growing research on M&A in family firms. It also increases our understanding of the boundary conditions of the FB brand effects, and the relevance that the previous reputation of a family firm can have in M&A scenarios. Finally, our study introduces the “Halo” and “Velcro” effects into the FB literature.
Investigating the impact of adopting big data and data analytics on enhancing audit quality
Authors: Abdelwahed Ahmed Saad , Abu-Musa Ahmad Abd El Salam , Badawy Hebatallah Abd El Salam , Moubarak Hosam 
Published: August 2024

Purpose
This study aims to empirically investigate the impact of adopting big data and data analytics (BD&A) on audit quality (AQ).

Design/methodology/approach
A questionnaire was distributed among audit practitioners working at audit firms in Egypt and 205 responses were collected. Partial least square structural equation modeling (PLS-SEM) was used to analyze and test research hypotheses.

Findings
The results reveal that BD&A has a direct significant positive effect on the audit process (AP) and auditor competence (AC). However, an insignificant impact of BD&A is found on audit fees (AF). In addition, the results indicate that BD&A has significant positive direct and indirect impacts on AQ.

Research limitations/implications
The results of this study will benefit several auditing stakeholders, such as audit firms, audit regulators, novice financial auditors and academic scholars.

Originality/value
This research is one of the earliest to empirically address the role of BD&A in enhancing AQ. It incorporates AP, AC and AF as mediators into a single model to explain the impact of BD&A on AQ. Also, it attempts to provide empirical evidence from a developing country with a less-regulated audit environment.
Customer loyalty model in Islamic bank: mosque perspective
Authors: Iskandar Ahmad Syarief , Muhajir Muhammad Nur Alam , Erwin Erwin , Fasiha Fasiha 
Published: August 2024

Purpose
This study aims to test the empirical Islamic bank customer loyalty model with the perspective of mosques as customers.

Design/methodology/approach
The type of research used is quantitative to collect data from mosque customers; 93 questionnaires were analyzed using partial least square-structural equation modeling.

Findings
This research found a significant relationship between service quality and perceived value, service quality and brand image, perceived value and customer satisfaction, brand image and customer loyalty and customer satisfaction with brand image.

Research limitations/implications
First, this study only collects data from certain organizations or communities so that further research can develop the model by adding several other communities or organizations. Second, this research does not include several other important variables that influence customer loyalty, such as product innovation and company capabilities.

Originality/value
Islamic bank customer loyalty models have been widely explored from the perspective of individual customers only. This research offers new attributes that influence customer loyalty models in the context of organizations or communities, namely, mosques.
The relationship between classroom racial/ethnic composition and teacher perceptions of work conditions
Authors: Barch Fabian , Cherng Hua-Yu Sebastian 
Published: August 2024

Purpose
Prior research reveals that teachers have lower job satisfaction when they have more Black students, but this work does not consider how different aspects of work conditions – and the increasing diversity of students beyond a Black/White binary – may matter. This study aims to examine the relationship between teachers’ perceptions of work conditions and the racial/ethnic compositions of the classes they teach.

Design/methodology/approach
This study leverages data from the Measures of Effective Teaching (MET) study. Analyses consist of both descriptive statistics and multilevel regression modeling.

Findings
Findings show that teachers’ satisfaction with working conditions varies in relation to the racial/ethnic composition of their students. Increase in the percent of Black, Latinx and Asian American students in a teacher’s classroom, was associated with a decrease in satisfaction with community involvement and student behavior. For increase in Latinx and Asian American students, this study finds significant decrease in satisfaction with measures of pedagogical and job support. For measures of school leadership and responsiveness to professional development needs, this study sees no significant relationship, which suggests that perceptions of some working conditions are more strongly tied to classroom demographics than others.

Originality/value
This work adds nuance to previous research on teacher job satisfaction by exploring satisfaction with various working conditions and how it varies in relation to classroom racial composition. This study concludes with a discussion of potential explanations for observed differences in teacher satisfaction, as well as potential ways to address these differences.
From general AI to custom AI: the effects of generative conversational AI’s cognitive and emotional conversational skills on user's guidance
Authors: Wang Kun , Pan Zhao , Lu Yaobin 
Published: August 2024

PurposeGenerative conversational artificial intelligence (AI) demonstrates powerful conversational skills for general tasks but requires customization for specific tasks. The quality of a custom generative conversational AI highly depends on users’ guidance, which has not been studied by previous research. This study uses social exchange theory to examine how generative conversational AI’s cognitive and emotional conversational skills affect users’ guidance through different types of user engagement, and how these effects are moderated by users’ relationship norm orientation.Design/methodology/approachBased on data collected from 589 actual users using a two-wave survey, this study employed partial least squares structural equation modeling to analyze the proposed hypotheses. Additional analyses were performed to test the robustness of our research model and results.FindingsThe results reveal that cognitive conversational skills (i.e. tailored and creative responses) positively affected cognitive and emotional engagement. However, understanding emotion influenced cognitive engagement but not emotional engagement, and empathic concern influenced emotional engagement but not cognitive engagement. In addition, cognitive and emotional engagement positively affected users’ guidance. Further, relationship norm orientation moderated some of these effects such that the impact of user engagement on user guidance was stronger for communal-oriented users than for exchange-oriented users.Originality/valueFirst, drawing on social exchange theory, this study empirically examined the drivers of users’ guidance in the context of generative conversational AI, which may enrich the user guidance literature. Second, this study revealed the moderating role of relationship norm orientation in influencing the effect of user engagement on users’ guidance. The findings will deepen our understanding of users’ guidance. Third, the findings provide practical guidelines for designing generative conversational AI from a general AI to a custom AI.
Damaging dichotomies and confounding contradictions in mental health inpatient nursing: lessons learned from Orwell’s 1984
Authors: Haslam Michael , Harding Keir 
Published: August 2024

Purpose
This discursive paper considers the use of restrictive practices in mental health inpatient settings and how these are often prioritised over relational approaches, especially where the diagnostic label of personality disorder intersects with risk.

Design/methodology/approach
Key concepts from Orwell’s 1984 are studied for their pertinence to mental health inpatient settings, supporting our argument that restrictive practices arise from dichotomous thinking and externalised fears.

Findings
Drawing upon Orwellian themes of power, social control and digital surveillance from 1984, the authors highlight the role of fear in perpetuating restrictive practices under a guise of benevolent care in mental health inpatient settings, especially for those who are diagnosed with a personality disorder. A lack of preparedness to work with complexity in such environments, coupled with a deficit in self-reflexivity and critical thinking, can exacerbate challenges.

Research limitations/implications
To transcend damaging dichotomies and reduce restrictive practices in inpatient settings, the authors make the argument for the adequate preparation and education of the mental health nurse and authentic, collaborative, user-involved care.

Originality/value
The authors use Orwell’s novel to support a critical discourse around those damaging dichotomies and inherent contradictions that contribute to restrictive practice in contemporary mental health settings and to question whose interests’ these restrictive practices serve.
Understanding customer’s intention to adopt metaverse banking services in Pakistan: a qualitative study
Authors: Hassaan Muhammad 
Published: August 2024

Purpose
The emergence of metaverse banking services (MBS) enables customers to interact and socialise in a virtual environment. However, there is a lack of research on MBS adoption. This study aims to examine the key factors influencing customer behaviour in adopting MBS, with a specific focus on Pakistan as a developing country.

Design/methodology/approach
Semi-structured interviews were conducted with 22 Pakistani banking customers, and the resulting data were transcribed and subjected to thematic analysis using NVivo software.

Findings
This qualitative investigation into the determinants of MBS adoption encompasses a wide range of facilitators, inhibitors and customer resources. These findings ultimately contribute fresh perspectives to the field, challenging prevailing beliefs and offering new insights into the complex dynamics driving customer behaviour in the MBS context.

Research limitations/implications
Since this study only focused on Pakistan with a limited scope, future studies on MBS adoption would benefit from a comparative analysis across several countries, especially in Asian nations.

Practical implications
This study advances our understanding of MBS adoption by revealing key determinants of customer intentions. Moreover, it offers actionable guidance for banking professionals, marketers and policymakers to navigate the implementation of MBS and unlock promising avenues for growth and innovation.

Originality/value
The first scholarly inquiry into MBS adoption seeks to expand extant knowledge by elucidating customers' viewpoints, thereby revealing novel insights into the key factors that influence customer behaviour within the MBS landscape.
Finite-time error constraint control for multi-AUV systems with unknown uncertainties based on observer and tan-type barrier Lyapunov function
Authors: Sun Yanchao , Li Jiayu , Qin Hongde , Du Yutong 
Published: August 2024

Purpose
Autonomous underwater vehicle (AUV) is widely used in resource prospection and underwater detection due to its excellent performance. This study considers input saturation, nonlinear model uncertainties and external ocean current disturbances. The containment errors can be limited to a small neighborhood of zero in finite time by employing control strategy. The control strategy can keep errors within a certain range between the trajectory followed by AUVs and their intended targets. This can mitigate the issues of collisions and disruptions in communication which may arise from AUVs being in close proximity or excessively distant from each other.

Design/methodology/approach
The tracking errors are constrained. Based on the directed communication topology, a cooperative formation control algorithm for multi-AUV systems with constrained errors is designed. By using the saturation function, state observers are designed to estimate the AUV’s velocity in six degrees of freedom. A new virtual control algorithm is designed through combining backstepping technique and the tan-type barrier Lyapunov function. Neural networks are used to estimate and compensate for the nonlinear model uncertainties and external ocean current disturbances. A neural network adaptive law is designed.

Findings
The containment errors can be limited to a small neighborhood of zero in finite time so that follower AUVs can arrive at the convex hull consisting of leader AUVs within finite time. The validity of the results is indicated by simulations.

Originality/value
The state observers are designed to approximate the speed of the AUV and improve the accuracy of the control method. The anti-saturation function and neural network adaptive law are designed to deal with input saturation and general disturbances, respectively. It can ensure the safety and reliability of the multiple AUV systems.
Digital twin–driven optimization of laser powder bed fusion processes: a focus on lack-of-fusion defects
Authors: Malik Asad Waqar , Mahmood Muhammad Arif , Liou Frank 
Published: August 2024

Purpose
The purpose of this research is to enhance the Laser Powder Bed Fusion (LPBF) additive manufacturing technique by addressing its susceptibility to defects, specifically lack of fusion. The primary goal is to optimize the LPBF process using a digital twin (DT) approach, integrating physics-based modeling and machine learning to predict the lack of fusion.

Design/methodology/approach
This research uses finite element modeling to simulate the physics of LPBF for an AISI 316L stainless steel alloy. Various process parameters are systematically varied to generate a comprehensive data set that captures the relationship between factors such as power and scan speed and the quality of fusion. A novel DT architecture is proposed, combining a classification model (recurrent neural network) with reinforcement learning. This DT model leverages real-time sensor data to predict the lack of fusion and adjusts process parameters through the reinforcement learning system, ensuring the system remains within a controllable zone.

Findings
This study's findings reveal that the proposed DT approach successfully predicts and mitigates the lack of fusion in the LPBF process. By using a combination of physics-based modeling and machine learning, the research establishes an efficient framework for optimizing fusion in metal LPBF processes. The DT's ability to adapt and control parameters in real time, guided by machine learning predictions, provides a promising solution to the challenges associated with lack of fusion, potentially overcoming the traditional and costly trial-and-error experimental approach.

Originality/value
Originality lies in the development of a novel DT architecture that integrates physics-based modeling with machine learning techniques, specifically a recurrent neural network and reinforcement learning.
Dissecting investment frequency: examining the role of social influence, investors’ perception of gender discrimination, involvement, access to information and risk tolerance
Authors: Singh Apoorva , Biswas Abhijeet 
Published: August 2024

Purpose
The recent economic changes in India and the gender discrimination practices of the patriarchal society have forced Indian women to turn to the financial sector as an essential means of generating returns. This study aims to identify the factors influencing investors’ investment frequency in India’s two most recognized metropolitan areas.

Design/methodology/approach
The authors applied structural equation modeling to augment Allport’s consumer behavior model and the social influence theory for assessing the frequency of investments made by 690 investors. The direct and indirect linkages in the proposed model were evaluated using moderation and mediation techniques.

Findings
The study’s findings show that investors’ perceptions of gender discrimination practices and social influence considerably increase investors’ involvement, magnifying their investment frequency. In addition, access to reliable information reinforces the relationship between investors’ involvement and their frequency of investments, whereas the low-risk tolerance weakens this association.

Research limitations/implications
The findings could help policymakers, investors, financial media outlets, financial experts, educational institutions and society strengthen India’s financial sector by leveraging the linkage between the underlying constructs and investors’ behavior.

Originality/value
The aspects of involvement and gender inequality have not garnered enough attention in the previous studies on behavioral finance. The study delves deeper into investor behavior by establishing a link between the underlying constructs and broadening the horizons of prominent consumer behavior models. It also unfurls the moderating role of access to information and risk tolerance to comprehend the association better.
Safety first! Psychological safety as a driver of learning in functionally diverse, self-assembled teams
Authors: Wax Amy , Asencio Raquel , Bentley Jeffrey R. , Warren Catherine 
Published: August 2024

Purpose
This study aims to explore psychological safety as a potential moderating mechanism for the relation between functional diversity and individual perceptions of learning, and functional diversity and team performance in self-assembled teams.

Design/methodology/approach
To test these relationships, the authors conducted a cross-level, time-lagged, quasi-experiment, using a sample of 143 self-assembled teams. In one condition, participants formed into functionally diverse teams, and in another condition, participants formed functionally homogeneous teams.

Findings
Results suggest that functional diversity and psychological safety have an interactive effect on both individual learning and self-assembled team performance, albeit in different directions. Specifically, low psychological safety was more deleterious for individuals on functionally diverse teams than functionally homogeneous teams when it came to perceptions of learning, but the opposite was true when it came to team performance.

Originality/value
The results of this study indicate that it is critical to train team members on developing psychological safety, both in traditional and functionally diverse contexts.
A technological biodiversity monitoring toolkit for biocredits
Authors: Ford Helen V. , Schrodt Franziska , Zieritz Alexandra , Exton Daniel A. , van der Heijden Geertje , Teague Jonathan , Coles Tim , Field Richard 
Published: August 2024

Abstract

Biodiversity is in crisis globally, and we consistently fail to hit global targets to stem its loss. Inspired by the Kunming‐Montreal Global Biodiversity Framework, the biodiversity credit market offers an avenue for vital funding for biodiversity conservation projects around the world. Various biodiversity credit methodologies and standards are becoming available, and most will require the measurement and monitoring of biodiversity at scale.

Private investment in conservation through biodiversity credits entails specific needs for biodiversity data collection, including the need for biodiversity claims to be verifiable. We conceptualise these requirements around ‘SAGED’ criteria: Scalable, Accessible, Granular (data of appropriate spatial, taxonomic and temporal resolution), Evidenceable and Directly measured (where possible).

Measuring and monitoring biodiversity across ecosystems, ecoregions and taxa is expensive and time‐consuming with traditional survey methods. These methods often rely on access to experts with sufficient taxonomic and survey expertise, which is challenging in many parts of the world. Accordingly, we review biodiversity monitoring technologies and assess their readiness to fulfil key requirements of assessments for the purpose of nature accounting for biodiversity credits (particularly SAGED criteria).

We focus on monitoring technologies that are commonly cited by biodiversity credit methodologies, including (e)DNA metabarcoding, passive acoustic monitoring and various other remote sensing methods. We also explore the current limits of these techniques in obtaining appropriate biodiversity measures and metrics for biodiversity finance.

Synthesis and applications. Technological solutions for biodiversity monitoring are not (yet) a panacea but are key for evidenceable monitoring at scale. For current use in biocredit markets, we advise these are combined with ground validation and human‐collected ecological data. Developments in automation and machine learning will rapidly make these technologies more accessible and efficient.
Determination of soluble solids content in tomatoes with different nitrogen levels based on hyperspectral imaging technique
Authors: Zhang Yiyang , Zhang Yao , Tian Yu , Ma Hua , Tian Xingwu , Zhu Yanzhe , Huang Yanfa , Cao Yune , Wu Longguo 
Published: August 2024

AbstractTomato is sweet and sour with high nutritional value, and soluble solids content (SSC) is an important indicator of tomato flavor. Due to the different mechanisms of nitrogen uptake and assimilation in plants, exogenous supply of different forms of nitrogen will have different effects on the growth, development, and physiological metabolic processes of tomato, thus affecting the tomato flavor. In this paper, hyperspectral imaging (HSI) technique combined with neural network prediction model was used to predict SSC of tomato under different nitrogen treatments. Competitive adaptive reweighed sampling (CARS) and iterative retained information variable (IRIV) were used to extract the feature wavelengths. Based on the characteristic wavelength, the prediction models of tomato SSC are established by custom convolutional neural network (CNN) model that was constructed and optimized. The results showed that the SSC of tomato was negatively correlated with nitrogen fertilizer concentration. For tomatoes treated with different nitrogen concentrations, the residual predictive deviation (RPD) of CARS‐CNN and IRIV‐parallel convolutional neural networks (PCNN) reached 1.64 and 1.66, both more than 1.6, indicating good model prediction. This study provides technical support for future online nondestructive testing of tomato quality.Practical ApplicationThe CARS‐CNN and IRIV‐PCNN were the best data processing model. Four customized convolutional neural networks were used for predictive modeling. The CNN model provides more accurate results than conventional methods.