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This study responded to calls to investigate the behavioural and social antecedents that produce a highly positive response to AI bias in a constrained region, which is characterised by a high share of people with minimal buying power, growing but untapped market opportunities and a high number of related businesses operating in an unregulated market.

Drawing on empirical data from 225 human resource managers from Ghana, data were sourced from senior human resource managers across industries such as banking, insurance, media, telecommunication, oil and gas and manufacturing. Data were analysed using a fussy set qualitative comparative analysis (fsQCA).

The results indicated that managers who regarded their response to AI bias as a personal moral duty felt a strong sense of guilt towards the unintended consequences of AI logic and reasoning. Therefore, managers who perceived the processes that guide AI algorithms' reasoning as discriminating showed a high propensity to address this prejudicial outcome.

As awareness of consequences has to go hand in hand with an ascription of responsibility; organisational heads have to build the capacity of their HR managers to recognise the importance of taking personal responsibility for artificial intelligence algorithm bias because, by failing to nurture the appropriate attitude to reinforce personal norm among managers, no immediate action will be taken.

By integrating the social identity theory, norm activation theory and justice theory, the study improves our understanding of how a collective organisational identity, perception of justice and personal values reinforce a positive reactive response towards AI bias outcomes.

This paper aims to explore the recognition and success of different ways of branding native advertising in influencer marketing.

The data are evaluated using statistical tests, correlation and cluster analysis.

It was found that the higher the recognition rate of a post tagged in a particular way, the better the tagging method for influencer marketing on Instagram. Based on the findings of this study, word tag is recommended first because it is flexible and has one of the highest recognition rates.

The generalizability of the results across different regional settings requires further investigation.

Good labeling of native advertising leads to greater success.

This study can be used by marketing managers, advertisers and influencers to gain insight into the issue, as well as to better select the appropriate labeling method for their advertising content.

Este trabajo tiene como objetivo explorar el reconocimiento y el éxito de diferentes formas de branding de publicidad nativa en el marketing de influencers.

Los datos se evalúan mediante pruebas estadísticas, correlación y análisis de conglomerados.

Se encontró que cuanto mayor es la tasa de reconocimiento de un post etiquetado de una manera particular, mejor es el método de etiquetado para el marketing de influencers en Instagram. Basándose en los resultados de este estudio, se recomienda en primer lugar el etiquetado por palabras porque es flexible y tiene una de las tasas de reconocimiento más altas.

Un buen etiquetado de la publicidad nativa conduce a un mayor éxito.

Este estudio puede ser utilizado por directores de marketing, anunciantes e influencers para obtener información sobre el tema, así como para seleccionar mejor el método de etiquetado adecuado para su contenido publicitario.

La generalizabilidad de los resultados en diferentes entornos regionales requiere más investigación.

本文旨在探讨影响者营销中不同方式的品牌原生广告的识别和成功。

使用统计测试、相关性和聚类分析对数据进行评估。

研究发现, 以特定方式标记的帖子的识别率越高, Instagram上影响者营销的标记方式就越好。基于这项研究的结果, 首先推荐单词标签, 因为它很灵活, 而且有最高的识别率之一。

对原生广告进行良好的标注会带来更大的成功。

本研究可供营销经理、广告商和影响者使用, 以深入了解这一问题, 并更好地为其广告内容选择合适的标签方法。

研究结果在不同地区环境中的普适性需要进一步调查。

Grounded in self-congruency theory, this study aims to investigate the impact of different types of congruence in social media influencer marketing on consumers’ online impulse buying intentions under the moderating influence of wishful identification.

This study collects survey responses from an online sample of 232 social media users and analyses them using partial least squares structural equation modelling.

This study delineates two distinct pathways influencing online impulse buying intentions within influencer marketing: direct consumer–product congruence and the conditional role of consumer–influencer congruence. Particularly, the alignment between a consumer’s self-image and the product’s attributes independently drives online impulse buying intentions. Conversely, consumer–influencer congruence, despite high alignment, fails to spur online impulse buying intentions unless amplified by wishful identification – the consumer’s aspirational desire to emulate the influencer. This finding underscores the complexity of impulsive consumer behaviours in the digital marketplace, highlighting the pivotal role of product appeal and the conditional influence of influencer relationships on spontaneous purchasing decisions.

This study pioneers by elucidating the congruence interplay between consumers, influencers and products in online impulse buying, emphasising wishful identification as a critical moderating factor. Theoretically, it expands self-congruency theory by detailing the distinct roles of congruence types on impulsive behaviours, notably underlining the essential role of wishful identification for the effect of consumer–influencer congruence. Practically, the insights equip brands with a deeper understanding of the key drivers behind impulsive purchases in an influencer-centric digital marketplace, offering strategic guidance for optimising influencer collaborations and product presentations to enhance consumer engagement and sales.

Strategic human capital analytics (SHCA) has proven to be promising for improved organizational performance; however, research remains unclear about its influence on new venture performance. Building on the dynamic capabilities view (DCV), this study investigates the relationship between SHCA and new venture performance via generative capabilities with the moderating role of dual nationality founding members.

A quantitative research study has been carried out. Data was collected via a survey form from 313 founding members of new tech ventures and analyzed using Hayes process macro model.

Research results show that the generative capability mediates the linkages between SHCA and new venture performance. Whereas, the dual nationality of a founding member strengthens the linkages between SHCA and generative capability due to their diverse perspective, larger networks, cognitive flexibility, and resilience, which are important for generative capabilities and SHCA.

The originality of these results lies in the exploration of the linkages between dual nationality and generative capability, as well as the special elements, such as diverse perspectives, larger networks, cognitive flexibility, and resilience, which are highlighted as possible advantages of dual nationality in the context of SHCA and new venture performance.

Deepor Beel is one of the Ramsar Site and a wetland of great biodiversity, situated in the south-western part of Guwahati, Assam. With urban development at its forefront city of Guwahati, Deepor Beel is under constant threat. The study aims to calculate the lake water volume from the water surface area and the underwater terrain data using a triangulated irregular network (TIN) volume model.

The lake water surface boundaries for each year were combined with field-observed water level data to generate a description of the underwater terrain. Time series LANDSAT images of 2001, 2011 and 2019 were used to extract the modified normalized difference water index (MNDWI) in GIS domain.

The MNDWI was 0.462 in 2001 which reduced to 0.240 in 2019. This shows that the lake water storage capacity shrank in the last 2 decades. This leads to a major problem, i.e. the storage capacity of the lake has been declining gradually from 20.95 million m³ in 2001 to 16.73 million m³ in 2011 and further declined to 15.35 million m³ in 2019. The fast decline in lake water volume is a serious concern in the age of rapid urbanization of big cities like Guwahati.

None of the studies have been done previously to analyze the decline in the volume of Deepor Beel lake. Therefore, this study will provide useful insights in the water resource management and the conservation of Deepor Beel lake.

Based on the social exchange theory, the aim of the present study is to examine the effects, both direct and indirect (through sustainability-oriented innovative behaviors [SIBs]), of sustainable project leadership (SPL) on sustainable project performance (SPP). Project management approaches (PMAs) (traditional, hybrid and agile) were examined as conditional factors in the “SPL–SIBs” relationship.

The study employs structural equation modeling based on data collected from 197 software engineering project team members working in the financial industry in Poland.

The study revealed that SPL significantly, positively affected SPP. It also provided evidence for the significant mediating impact of SIBs in the relationship between SPL and SPP and the conditional effect of agile and hybrid PMAs on the “SPL–SIBs” relationship.

The novelty of this work lies in introducing sustainable leadership into project management research, proposing and testing a unique and complex research framework, designing valid scales for measuring SPL and SPP, and suggesting many theoretical and empirical implications.

The purpose of this study is to understand the profit implications of analytics-driven centralized discriminatory pricing at the headquarter level compared with sales force price delegation in the purchase of an aftermarket good through an indirect retail channel with symmetric information.

Using individual-level loan application and approval data from a North American financial institution and segment-level customer risk as the price discrimination criterion for the firm, the authors develop a three-stage model that accounts for the salesperson’s price decision within the limits of the latitude provided by the firm; the firm’s decision to approve or not approve a sales application; and the customer’s decision to accept or reject a sales offer conditional on the firm’s approval. Next, the authors compare the profitability of this sales force price delegation model to that of a segment-level centralized pricing model where agent incentives and consumer prices are simultaneously optimized using a quasi-Newton nonlinear optimization algorithm (i.e. Broyden–Fletcher–Goldfarb–Shanno algorithm).

The results suggest that implementation of analytics-driven centralized discriminatory pricing and optimal sales force incentives leads to double-digit lifts in firm profits. Moreover, the authors find that the high-risk customer segment is less price-sensitive and firms, upon leveraging this segment’s willingness to pay, not only improve their bottom-line but also allow these marginalized customers with traditionally low approval rates access to loans. This points out the important customer welfare implications of the findings.

Substantively, to the best of the authors’ knowledge, this paper is the first to empirically investigate the profitability of analytics-driven segment-level (i.e. discriminatory) centralized pricing compared with sales force price delegation in indirect retail channels (i.e. where agents are external to the firm and have access to competitor products), taking into account the decisions of the three key stakeholders of the process, namely, the consumer, the salesperson and the firm and simultaneously optimizing sales commission and centralized consumer price.

As social networks have developed to be a ubiquitous platform of public opinion spreading, it becomes more and more crucial for maintaining social security and stability by accurately predicting various trends of public opinion dissemination in social networks. Considering the fact that the dissemination of online public opinion is a dynamic process full of uncertainty and complexity, this study establishes a novel conformable fractional discrete grey model with linear time-varying parameters, namely the CFTDGM(1,1) model, for more accurate prediction of online public opinion trends.

First, the conformable fractional accumulation and difference operators are employed to build the CFTDGM(1,1) model for enhancing the traditional integer-order discrete grey model with linear time-varying parameters. Then, to improve forecasting accuracy, a base value correction term is introduced to optimize the iterative base value of the CFTDGM(1,1) model. Next, the differential evolution algorithm is selected to determine the optimal order of the proposed model through a comparison with the whale optimization algorithm and the particle swarm optimization algorithm. The least squares method is utilized to estimate the parameter values of the CFTDGM(1,1) model. In addition, the effectiveness of the CFTDGM(1,1) model is tested through a public opinion event about “IG team winning the championship”. Finally, we conduct empirical analysis on two hot online public opinion events regarding “Chengdu toddler mauled by Rottweiler” and “Mayday band suspected of lip-syncing,” to further assess the prediction ability and applicability of the CFTDGM(1,1) model by comparison with seven other existing grey models.

The test case and empirical analysis on two recent hot events reveal that the CFTDGM(1,1) model outperforms most of the existing grey models in terms of prediction performance. Therefore, the CFTDGM(1,1) model is chosen to forecast the development trends of these two hot events. The prediction results indicate that public attention to both events will decline slowly over the next three days.

A conformable fractional discrete grey model is proposed with the help of conformable fractional operators and a base value correction term to improve the traditional discrete grey model. The test case and empirical analysis on two recent hot events indicate that this novel model has higher accuracy and feasibility in online public opinion trend prediction.

This review aims to give a critical view of the wheel/rail high frequency vibration-induced vibration fatigue in railway bogie.

Vibration fatigue of railway bogie arising from the wheel/rail high frequency vibration has become the main concern of railway operators. Previous reviews usually focused on the formation mechanism of wheel/rail high frequency vibration. This paper thus gives a critical review of the vibration fatigue of railway bogie owing to the short-pitch irregularities-induced high frequency vibration, including a brief introduction of short-pitch irregularities, associated high frequency vibration in railway bogie, typical vibration fatigue failure cases of railway bogie and methodologies used for the assessment of vibration fatigue and research gaps.

The results showed that the resulting excitation frequencies of short-pitch irregularity vary substantially due to different track types and formation mechanisms. The axle box-mounted components are much more vulnerable to vibration fatigue compared with other components. The wheel polygonal wear and rail corrugation-induced high frequency vibration is the main driving force of fatigue failure, and the fatigue crack usually initiates from the defect of the weld seam. Vibration spectrum for attachments of railway bogie defined in the standard underestimates the vibration level arising from the short-pitch irregularities. The current investigations on vibration fatigue mainly focus on the methods to improve the accuracy of fatigue damage assessment, and a systematical design method for vibration fatigue remains a huge gap to improve the survival probability when the rail vehicle is subjected to vibration fatigue.

The research can facilitate the development of a new methodology to improve the fatigue life of railway vehicles when subjected to wheel/rail high frequency vibration.

This study aims to investigate the satisfaction of farmers with the compensation policy for wildlife-caused damages and its influencing factors, analyze the current situation of satisfaction with the compensation policy among farmers, identify factors significantly affecting satisfaction, and explore ways to optimize the compensation policy and improve the satisfaction of farmers based on the effects of various influencing factors.

The Xishuangbanna National Nature Reserve in Yunnan Province, China, is selected as the research area for the study. Through field interviews, 370 valid questionnaires were collected to obtain relevant data on farmers' satisfaction with the compensation policy for wildlife-caused damages. The Oprobit model is utilized to explore the factors influencing farmer satisfaction and to analyze their underlying reasons.

The study reveals that farmers in the communities surrounding the Xishuangbanna National Nature Reserve generally experience low satisfaction with the compensation policy, particularly concerning satisfaction with compensation amounts, which tends to be dissatisfied on average. Satisfaction with the compensation policy is significantly influenced by individual characteristics and household labor structure, while the degree of human-wildlife conflict, wildlife conservation attitudes and household income structure have insignificant impact. Among individual characteristics, gender, education level, health status, and ethnicity are highly significant. In household labor structure, the number of agricultural laborers, non-agricultural laborers, and household agricultural labor time are highly significant.

Building on the overall satisfaction of farmers with the compensation policy, this study further decomposes policy satisfaction into satisfaction with compensation amounts, coverage, and procedures. It provides more targeted recommendations for enhancing satisfaction with the compensation policy, which can help effectively mitigate human-wildlife conflicts and achieve harmonious coexistence between humans and nature.