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Given that the literature on the links between taxation and inclusive human development is ambiguous, it is important to investigate whether the mediating influence of governance in taxation for inclusive development exists. Thus, this study aims to explore the linkages between the governance quality, taxation and inclusive human development (i.e. inequality-adjusted human development index).

This study employs the generalized method of moments (GMM) technique to establish the empirical findings on 52 African countries for the period 2010–2018. Among the existing GMM approaches, this study follows the Roodman approach, an enhancement of the Arellano and Bover techniques, which limits the proliferation of instruments. This study uses the two-step approach, which deals with issues of the heteroscedasticity as against instead the one-step procedure, which solely addresses the homoscedasticity concerns.

The following findings are established. First, there is an unconditional positive effect of taxation on inclusive human development. Second, the net effects of taxation on inclusive human development, associated with the interaction of the government revenue with governance quality variables, are positive for the most part. It is then evident that when taxation policies are combined with good governance initiatives, the ultimate impact of inclusive human development is likely to be enhanced.

This study establishes that, whereas taxation dynamics largely have a favorable incidence in promoting inclusive human development, when such taxation measures are complemented with good governance initiatives, the overall impact of inclusive human development is also likely to be positive. It follows that policies designed to promote political, economic and institutional governance should be implemented in tandem, which policies designed to boost tax performance in the sampled countries. The findings can also be understood from the perspectives that inclusive human development is likely to be boosted when taxation measures are complemented with, (1) the free and fair election and replacement of political leaders (i.e. political governance), (2) the formulation and implementation of inclusive policies for the delivery of public goods (i.e. economic governance) and (3) the respect by citizens and the state of institutions that govern interactions between them (i.e. institutional governance).

This study aims to examine how dynamic pricing impacts customer perceptions of restaurants and sentiment toward prices via online reputation metrics. In addition, to deepen the debate on dynamic pricing, a novel definition is drawn by exploring the specific forms of discrimination that can manifest in different industries.

Leveraging a comprehensive data set of restaurant reviews sourced from TripAdvisor, the study focuses on restaurants affiliated with one of the largest groups of restaurants in Spain. We used a quasi-experimental method (difference-in-differences), to study how dynamic pricing strategies influence customers’ perceptions of value based on numerical ratings. Meanwhile, we used a Bidirectional Encoder Representations from Transformers model on the textual component of reviews to dissect the emotional nuances of dynamic pricing.

Results did not reveal a causal impact of dynamic pricing strategies on customers’ perceptions. Moreover, the sentiment analysis shows no heightened negative view after introducing dynamic pricing in restaurants compared to the control group. Contrary to what previous literature suggests, our findings indicate that implementing dynamic pricing does not adversely affect customers’ perceptions or sentiments regarding prices in restaurants.

The quasi-experimental setting of the study presents inherent challenges in establishing causality that require further investigation using controlled experimental settings. Nevertheless, our study reveals that restaurant customers do not perceive dynamic pricing as unfair. This finding is critical for restaurant managers when considering the implementation of dynamic pricing and revenue management strategies. In addition, our study highlights the importance of considering not only numerical ratings but customer sentiment analysis as well. This more holistic approach to assessing the impact of pricing strategies can give restaurant managers a deeper understanding of customer reactions. In addition, a more rigorous definition of dynamic pricing is provided, clarifying its nature and its distinction in using different price discrimination.

This study contributes to the evolving understanding of dynamic pricing strategies’ impact on customers’ perceptions and sentiments in the restaurant industry. It aims to fill the gap in understanding customer reactions to algorithmically determined prices (via revenue management systems such as DynamEat) in this industry. The combination of causal inference and sentiment analysis offers a novel perspective, shedding light on the nuanced connections between dynamic pricing implementation and customers’ emotions.

本研究考察动态定价如何通过在线声誉指标影响顾客对餐厅的感知和对价格的情绪。此外, 为了深化对动态定价的讨论, 通过探索不同行业中可能表现出的具体歧视形式, 提出了一个新的定义。

利用从TripAdvisor获取的餐厅评论的全面数据集, 研究聚焦于与西班牙最大的餐厅集团之一相关联的餐厅。我们采用了准实验方法（差异中的差异）, 研究动态定价策略如何根据数值评分影响顾客对价值的感知。同时, 我们运用BERT模型对评论的文本成分进行分析, 以解析动态定价的情感细微差别。

结果没有揭示动态定价策略对顾客感知产生因果影响。此外, 情绪分析显示, 在餐厅引入动态定价后, 与对照组相比, 没有增加消极观点。与以往文献所述相反, 我们的发现表明, 实施动态定价并不会对顾客对价格的感知或情绪产生负面影响。

研究的准实验设置存在确立因果关系的固有挑战, 需要通过控制实验设置进一步调查。尽管如此, 我们的研究揭示了餐厅顾客不认为动态定价不公平。这一发现对餐厅经理在考虑实施动态定价和收入管理策略时至关重要。此外, 我们的研究强调, 考虑顾客情绪分析和数值评分的重要性。这种更全面的方法评估定价策略的影响, 可以让餐厅经理更深入地理解顾客反应。此外, 提供了一个更严格的动态定价定义, 澄清了其性质及其在使用不同价格歧视中的区别。

本研究对于理解动态定价策略对餐厅行业顾客感知和情绪影响的不断发展有所贡献。它旨在填补对客户对算法确定的价格（通过收入管理系统（RMS）例如DynamEat）在此行业中反应的理解空白。因果推断与情绪分析的结合提供了新的视角, 揭示了动态定价实施与顾客情绪之间微妙的联系。

Este estudio examina cómo la fijación dinámica de precios impacta en las percepciones de los clientes de los restaurantes y en el sentimiento hacia los precios a través de métricas de reputación en línea. Además, para profundizar en el debate sobre la fijación dinámica de precios, se propone una definición novedosa explorando las formas específicas de discriminación que pueden manifestarse en diferentes industrias.

Utilizando un conjunto de datos exhaustivo de reseñas de restaurantes obtenidas de TripAdvisor, el estudio se centra en los restaurantes afiliados a uno de los mayores grupos de restaurantes en España. Empleamos un método cuasiexperimental (diferencias en diferencias) para estudiar cómo las estrategias de precios dinámicos influyen en las percepciones de valor de los clientes basándonos en las calificaciones numéricas. Mientras tanto, empleamos un modelo BERT en el componente textual de las reseñas para desentrañar los matices emocionales de la fijación dinámica de precios.

Los resultados no revelaron un impacto causal de las estrategias de precios dinámicos en las percepciones de los clientes. Además, el análisis de sentimiento no muestra una visión negativa aumentada después de introducir la fijación dinámica de precios en los restaurantes en comparación con el grupo de control. Contrariamente a lo que sugiere la literatura previa, nuestros hallazgos indican que la implementación de precios dinámicos no afecta negativamente las percepciones o los sentimientos de los clientes respecto a los precios en los restaurantes.

La configuración cuasiexperimental del estudio presenta desafíos inherentes para establecer la causalidad que requieren una investigación más profunda utilizando entornos experimentales controlados. Sin embargo, nuestro estudio revela que los clientes de restaurantes no perciben la fijación de precios dinámica como injusta. Este hallazgo es crítico para los gerentes de restaurantes al considerar la implementación de la fijación de precios dinámica y estrategias de gestión de ingresos. Además, nuestro estudio resalta la importancia de considerar no solo las calificaciones numéricas sino también el análisis del sentimiento del cliente. Este enfoque más holístico para evaluar el impacto de las estrategias de precios puede dar a los gerentes de restaurantes una comprensión más profunda de las reacciones de los clientes. Además, se proporciona una definición de fijación de precios dinámica más rigurosa, aclarando su naturaleza y su distinción en el uso de diferentes discriminaciones de precios.

Este estudio contribuye a la comprensión en evolución del impacto de las estrategias de fijación de precios dinámicos en las percepciones y sentimientos de los clientes en la industria restaurantera. Su objetivo es llenar el vacío en la comprensión de las reacciones de los clientes a los precios determinados algorítmicamente (a través de sistemas de gestión de ingresos (RMS) como DynamEat) en esta industria. La combinación de inferencia causal y análisis de sentimientos ofrece una perspectiva novedosa, arrojando luz sobre las conexiones matizadas entre la implementación de la fijación de precios dinámicos y las emociones de los clientes.

Extant studies in entrepreneurship have explored factors that influence the birth and growth of start-up firms; however, there appears to be a dearth of studies examining the influence of founders' ambidextrous orientation on start-ups' success, especially their speed of attaining the coveted status of a “unicorn start-up” – which is considered a mega success in practice. This study examines whether and how founding teams’ collective ambidextrous orientation influences their respective start-ups’ pace of becoming a “unicorn”.

This study empirically analyses 220 interviews by the founders of 83 Indian unicorns in examining the influence of the founding teams’ collective exploration-exploitation capability on their firms' speed to achieve the “unicorn” status. The Cox Hazard model was used to test the hypothesized relationships, and linear ordinary least squares (OLS) regression was used to test the robustness of the results.

The authors find a strong positive relationship between founding teams’ ambidextrous skills and the speed of becoming a unicorn. The study results suggest that the founding teams’ collective exploratory skills may be more influential in their start-up’s speed to unicorn status vis-à-vis their exploitative skills.

This study finds that the founding teams’ ambidextrous orientation and exploratory skills accelerate their start-up’s speed to becoming a unicorn, contributing to the academic discourse on the “unicorn” phenomenon, which is widely acknowledged as a grand success status for start-ups—especially technology and venture capital funded start-ups—among the practitioners. This study contributes to the academic discourse on firm capabilities and founding-team-related antecedents of start-up success by raising a new dimension of the founding team’s ambidextrous orientation.

This study, anchored in Self-Determination Theory, explores the paradoxical effects of gamification on digital health promotion, focusing on user engagement, physical activity adherence, stress levels and social dynamics.

In a preregistered experimental design, participants interacted with one of three fitness apps: a non-gamified control app, a competition-based gamified app (GameFit) or a self-exploration-based gamified app (ExploreFit). Quantitative data were collected through scales measuring key variables, and qualitative insights were gathered from structured diaries.

Gamification significantly enhanced user engagement and physical activity adherence. However, competition-based gamification also increased stress and negative social dynamics, with gender-specific effects: females experienced higher stress and males encountered more negative social dynamics. These adverse effects were mitigated in the ExploreFit group, suggesting that self-exploration elements in gamification can reduce negative outcomes.

This research offers a comprehensive analysis of the complex interplay between gamification, competition, self-exploration and health outcomes. It provides valuable insights for the design of persuasive technologies, highlighting the need for a balanced approach to gamification that considers both its benefits and potential pitfalls.

This paper aims to investigate the entrepreneurial behaviours exhibited by commercial smallholder farmers in Zimbabwe, focusing on their socio-economic characteristics, and considers their implication for outcomes of livelihood resilience in a resource-constrained and turbulent rural context.

The study used survey data collected from 430 smallholder farmers in Masvingo province, Zimbabwe. Using a two-step cluster analysis, the study constructed a typology of farmers based on their entrepreneurial behaviour and socio-economic characteristics.

The results revealed that commercial smallholder farmers are heterogeneous in terms of their entrepreneurial behaviours. Four clusters were identified: non-entrepreneurial, goal-driven, means-driven and ambidextrous. Beyond their entrepreneurial behaviours, these clusters significantly differ in the socio-economic characterises (gender, age, education levels, farm size, proximity to the market and social connection) and farm performance (seasonal sales per hectare and farm income per hectare).

The typology framework relating farmers’ entrepreneurial behaviours to their socio-economic characteristics and business performance is important to tailor and therefore improve the effectiveness of farmer entrepreneurship programmes and policies. In particular, tailoring farmer entrepreneurship education is crucial to distribute land, finance and market resources in purposive ways to promote a combination of smallholder farmers’ effectual and causal behaviours at an early stage of their farm ventures.

Researchers still know little about which farmers’ behaviours are entrepreneurial and how these behaviours manifest in action during their commercial farm activities. This research leverages effectuation and causation theory to unveil previously overlooked distinctions on farmers’ entrepreneurial behaviours, thereby enhancing a more grounded understanding of farmer entrepreneurship in a resource-constrained context.

This study uses computer-aided design to improve the ecological and environmental sustainability of early-stage landscape designs. Urban expansion on open land and natural habitats has led to a decline in biodiversity and increased climate change impacts, affecting urban inhabitants' quality of life and well-being. While sustainability indicators have been employed to assess the performance of buildings and neighbourhoods, landscape designs' ecological and environmental sustainability has received comparatively less attention, particularly in early-design stages where applying sustainability approaches is impactful.

The authors propose a computation framework for evaluating key landscape sustainability indicators and providing real-time feedback to designers. The method integrates spatial indicators with widely recognized sustainability rating system credits. A specialized tool was developed for measuring biomass optimization, precipitation management and urban heat mitigation, and a proof-of-concept experiment tested the tool's effectiveness on three Mediterranean neighbourhood-level designs.

The results show a clear connection between the applied design strategy to the indicator behaviour. This connection enhances the ability to establish sustainability benchmarks for different types of landscape developments using parametric design.

The study allows non-expert designers to measure and embed landscape sustainability early in the design stages, thus lowering the entry level for incorporating biodiversity enhancement and climate mitigation approaches.

This study expands the parametric vocabulary for measuring landscape sustainability by introducing spatial ecosystem services and architectural sustainability indicators on a unified platform, enabling the integration of critical climate and biodiversity-loss solutions earlier in the development process.

In order to determine whether the factors affecting office worker well-being are location dependent, this exploratory study analyses the relative importance of different “building well-being” factors for prime office workers in two leading but environmentally contrastive real estate markets: London and Hong Kong.

This paper adopts a mixed methods sequential explanatory design (follow-up explanations model), consisting of three phases: an exploratory phase to refine the building well-being factors, a quantitative phase utilising a questionnaire to assess the relative importance of these building well-being factors (N = 281: London = 171; Hong Kong = 110), followed by a final phase of follow-up interviews with respondents to explore the reasons behind the significant differences observed in the quantitative phase (N = 13: London = 7; Hong Kong = 6).

While London and Hong Kong share some highly-ranking factors in common, significant differences in importance are observed for 17 of the 31 identified factors as a result of contrasting physical, economic, and cultural environments.

Despite growing recognition of the importance of the built environment on well-being, to the authors’ knowledge there has been no previous research investigating how building well-being demands may vary systematically across geographies. Understanding these differences has important implications for interpreting building well-being research, effective business operations, real estate investment, building certification scheme design, and governance of the built environment.

This paper aims to address the challenges of the capacitor tower maintenance robot during bolt tightening in high-voltage substations, including difficulties in bolt positioning due to tilted angles and anti-bird cover occlusion and issues with fast and accurate docking of bolts while the base is moving.

This paper proposes a visual servoing method for the capacitor tower maintenance robot, including bolt pose estimation and visual servoing control. Bolt pose estimation includes four components: constructing a keypoint detection network to identify the approximate position, precise positioning, rapid prediction and calculation of bolt pose. In visual servoing, an improved position-based visual servoing (PBVS) is proposed, which eliminate steady-state error and enhance response speed during dynamic tracking by incorporating integral and differential components.

The bolt detection method exhibits high robustness against varying lighting conditions, partial occlusions, shooting distances and angles. The maximum positioning error at a distance of 250 mm is 2.8 mm. The convergence speed of the improved PBVS is 10% higher than that of the traditional PBVS when the base and target remain relatively stationary. When the base moves at a constant speed, the improved method eliminates steady-state error in dynamic tracking. When the base moves rapidly and intermittently, the maximum error of the improved method in the tracking process is 30% smaller than that of traditional PBVS.

This method enables real-time detection and positioning of bolts in an unstructured environment with tilt angles, variable lighting conditions and occlusion by anti-bird covers. An improved PBVS is proposed to enhance its capability in tracking dynamic targets.

In recent years, there has been a growing emphasis on competency-based systems as a means of assessing employee performance. These systems assess the degree to which the competencies of employees align with the requirements of their employment positions. This study aims to identify, prioritize, and make contextual interrelationships of the competency dimensions that are relevant for evaluating employees in the context of Indian manufacturing MSMEs.

These dimensions were identified through an extensive literature review and interviews with industry experts. Further, a mixed-methods approach, including the “Bayesian Best-Worst Method” (BBWM), is applied for prioritizing important dimensions, whereas for making mutual relationships, the “Interpretive Structural Modeling” (ISM) method is utilized. “Matrice d'impacts croisés multiplication appliquée á un classment” (MICMAC) is also known as “cross-impact matrix multiplication applied to classification” is used for clustering competency dimensions based on their “driving power” and “dependence power”.

The findings reveal that among the primary dimensions, “creative performance,” and among the sub-dimensions, “innovative behaviors,” are the most critical competency dimensions for an employee assessment. The study also found that “smart working”, “factual and theoretical knowledge”, “empathy at work”, “understanding of specific knowledge”, and “engagement ideas and activities” are the main dimensions driving employees' competency.

This paper provides contribution to the competence literature by identifying and evaluating competency dimensions for assessing employees' performance within manufacturing MSMEs in an emerging economy such as India. The study also assesses the rank and contextual relationship between the identified dimensions as no past research focused on the same by using BBWM and ISM in the Indian manufacturing MSMEs context.

A new Chatbot system is implemented to provide both voice-based and textual-based communication to address student queries without any delay. Initially, the input texts are gathered from the chat and then the gathered text is fed to pre-processing techniques like tokenization, stemming of words and removal of stop words. Then, the pre-processed data are given to the Natural Learning Process (NLP) for extracting the features, where the XLnet and Bidirectional Encoder Representations from Transformers (BERT) are utilized to extract the features. From these extracted features, the target-based fused feature pools are obtained. Then, the intent detection is carried out to extract the answers related to the user queries via Enhanced 1D-Convolutional Neural Networks with Long Short Term Memory (E1DCNN-LSTM) where the parameters are optimized using Position Averaging of Binary Emperor Penguin Optimizer with Colony Predation Algorithm (PA-BEPOCPA). Finally, the answers are extracted based on the intent of a particular student’s teaching materials like video, image or text. The implementation results are analyzed through different recently developed Chatbot detection models to validate the effectiveness of the newly developed model.

A smart model for the NLP is developed to help education-related institutions for an easy way of interaction between students and teachers with high prediction of accurate data for the given query. This research work aims to design a new educational Chatbot to assist the teaching-learning process with the NLP. The input data are gathered from the user through chats and given to the pre-processing stage, where tokenization, steaming of words and removal of stop words are used. The output data from the pre-processing stage is given to the feature extraction phase where XLnet and BERT are used. In this feature extraction, the optimal features are extracted using hybrid PA-BEPOCPA to maximize the correlation coefficient. The features from XLnet and features from BERT were given to target-based features fused pool to produce optimal features. Here, the best features are optimally selected using developed PA-BEPOCPA for maximizing the correlation among coefficients. The output of selected features is given to E1DCNN-LSTM for implementation of educational Chatbot with high accuracy and precision.

The investigation result shows that the implemented model achieves maximum accuracy of 57% more than Bidirectional long short-term memory (BiLSTM), 58% more than One Dimansional Convolutional Neural Network (1DCNN), 59% more than LSTM and 62% more than Ensemble for the given dataset.

The prediction accuracy was high in this proposed deep learning-based educational Chatbot system when compared with various baseline works.