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The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

The current estimation practice in construction projects greatly needs upgrading, as there has been no improvement in the cost overrun issue over the past 70 years. The purpose of this research was to develop a new multiple regression analysis (MRA)-based model to forecast the final cost of road projects at the pre-design stage using data from 43 projects in New Zealand (NZ).

The research used the case study of 43 completed road projects in NZ. Document analysis was conducted to collect data, and statistical tests were used for model development and analysis.

Eight models were developed, and all models achieved the required F statistics and met the regression assumptions. The models’ mean absolute percentage error (MAPE) was between 21.25% and 22.77%. The model with the lowest MAPE comprised the road length and width, number of bridges, pavement area, cut and fill area, preliminary cost and cost indices change.

The model is based on road projects in NZ. However, it was designed to be able to adapt to other contexts. The findings suggest that the model can be used to improve traditional conceptual estimating methods. Past project data is often stored by the project team but rarely used for analysing and forecasting purposes. This research emphasises that past data can be effectively used to predict the project cost at the pre-design stage with limited information.

No research was conducted to adopt cost modelling techniques into the conceptual estimation practice in the NZ construction industry.

In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.

We propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.

The literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.

Based on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.

This study examines how the strong emphasis placed on the purposes of budgeting, referring to a comprehensive focus on budgeting, is related to top managers' education and tenure while controlling for their functional positions in their respective firms and ages, as well as several company-specific predictors (information quality, firm size, information technology, importance of profit and strategy).

Survey data were collected from senior managers of large manufacturing firms in Finland and Sweden.

The results suggest that academic business education is positively associated with a comprehensive focus on budgeting, but tenure as well as functional position in the company (Chief Financial Officer (CFO) or not) and age are not. Overall, the company-specific control variables in general and information quality in particular are shown to have greater explanatory power than the top management characteristics analyzed.

This study identifies several empirically supported factors that seem to contribute to a comprehensive focus on budgeting. The effects of information quality, business education, the importance of profit and firm size could be considered in future research.

Academic business education matters more than the other top management characteristics analyzed. If organizations want to make comprehensive use of budgets, they should employ business graduates and be mindful of company-specific variables.

This study is the first to address a comprehensive focus on budgeting and some of its determinants. Future research could investigate a broader set of such determinants in different contexts.

The purpose of this paper is to have a deeper understanding of the nonlinear relationship between the impact of climate change on tourism development. Current studies on the effects of climate change on tourism development primarily rely on linear correlation assumptions.

Based on the New Institutional Economics theory, the institutional setting inherently motivates and ensures the growth of the tourism industry. For a precise evaluation of the nonlinear consequences of climate change on tourism, this paper concentrates on Chinese cities between 2011 and 2021, methodically analyzing the influence of climate change on tourism.

The study findings suggest that there is an “inverse U”-shaped nonlinear relationship between climate change and tourism development, initially strengthening and subsequently weakening. Based on these findings, the research further delves into how institutional contexts shape the nonlinear association between climate change and tourism growth. It was found that in a higher institutional backdrop, the “inverse U” curve tends to flatten and surpass the curve adjusted for a lesser institutional context. Upon deeper mechanism analysis, it was observed that cities with more advanced marketization, improved industrial restructuring and enhanced educational growth exhibit a more evident “inverse U”-shaped nonlinear connection between climate change and tourism evolution.

First, previous studies on climate change and tourism development largely rely on questionnaire data (Hu et al., 2022). In contrast to these studies, this paper uses dynamic panel data, which to some extent overcomes the subjectivity and difficulty of causality identification in questionnaire data, making our research conclusions more accurate and reliable. Second, this study breaks through the linear relationship hypothesis of previous literature regarding climate change and tourism development. By evaluating the nonlinear relationship of climate change to tourism development from the institutional pressure perspective, it more intricately delineates their interplay mechanism, expanding and supplementing the research literature on the relationship mechanism between climate change and tourism development. Thirdly, the conclusions of this study are beneficial for policymakers to better understand and assess the scope of climate change impacts. It also aids relevant departments in clarifying the direction of institutional environment optimization to elevate the level of tourism development when faced with adverse impacts brought about by climate change.

Foreign direct investment in the real estate (FDIRE) sector is required to bridge the gap between investment needed and domestic funds. Further, foreign direct investment is gaining importance because other sources of raising finance such as External Commercial Borrowing and foreign currency convertible bonds have been banned in the Indian real estate sector. Therefore, the objective of the study is to explore the determinants attracting foreign direct investment in real estate and to assess the impact of those variables on foreign direct investments in real estate.

Johansen cointegration test, vector error correction model along with variance decomposition and impulse response function are employed to understand the nexus of the relationship between various macroeconomic variables and foreign direct investment in real estate.

The results indicate that infrastructure, GDP and tourism act as drivers of foreign direct investment in real estate. However, interest rates act as a barrier.

This article aimed at exploring factors attracting FDIRE along with estimating the impact of identified variables on FDI in real estate. Unlike other studies, this study considers FDI in real estate instead of foreign real estate investments.

This paper analyses the factors that make fashion-product crowdfunding campaigns successful. The authors argue that crowdfunding is an innovative and functional way of bringing new fashion items to the market. The purpose of this paper is to answer the question whether product innovation, lifecycle and sustainability have a positive effect on the success of fashion crowdfunding campaigns. The findings highlight that the success of the fashion crowdfunding campaigns depends on creators' adherence to the values of the platform which they use to raise capital.

A total of 300 fashion crowdfunding projects running between the 17th of October and the 15th of December 2017 were collected from Kickstarter – the world's largest crowdfunding platform based on reward-based all-or-nothing model. Two-step binomial logistic regression was used to analyse the data.

The model predicted a significant increase in the odds of success for the fashion items crowdfunded during the first-time production, and innovative and environmentally sustainable products with a higher price range of rewards. In line with previous literature, regression analyses predicted a significant effect of the control variables of goal amount (negative) and the number of rewards (positive). Contrary to previous studies, neither the presence of a video nor the campaign length predicted success.

The novel findings of this study contribute to the literature by providing an analysis of success factors of fashion items on crowdfunding platforms. The results show that innovative, environmentally sustainable and higher-priced products produced by early-stage ventures are better welcomed by the audiences.

The purpose of this paper is to propose a semiparametric estimator for the tail index of Pareto-type random truncated data that improves the existing ones in terms of mean square error. Moreover, we establish its consistency and asymptotic normality.

To construct a root mean squared error (RMSE)-reduced estimator of the tail index, the authors used the semiparametric estimator of the underlying distribution function given by Wang (1989). This allows us to define the corresponding tail process and provide a weak approximation to this one. By means of a functional representation of the given estimator of the tail index and by using this weak approximation, the authors establish the asymptotic normality of the aforementioned RMSE-reduced estimator.

In basis on a semiparametric estimator of the underlying distribution function, the authors proposed a new estimation method to the tail index of Pareto-type distributions for randomly right-truncated data. Compared with the existing ones, this estimator behaves well both in terms of bias and RMSE. A useful weak approximation of the corresponding tail empirical process allowed us to establish both the consistency and asymptotic normality of the proposed estimator.

A new tail semiparametric (empirical) process for truncated data is introduced, a new estimator for the tail index of Pareto-type truncated data is introduced and asymptotic normality of the proposed estimator is established.

Individual differences cause many differences in human behaviour, and the first source of these differences is personality. In various organisations, employees are encouraged to manage conflict through conflict management styles. The way people think can be an essential factor in their ability to conflict management. Difficult employees are individuals who constantly use problematic communication styles to express their feelings and thoughts to direct the behaviour of others. This empirical study aims to investigate the effect of thinking styles on individuals’ conflict management in dealing with difficult personalities.

To achieve the research purpose, a gamified situation was designed, and a survey was performed in laboratory settings and on an online platform. At first, participants’ reactions were measured in the simulated conflict management situation dealing with difficult personalities; subsequently, the dominant thinking style of participants was measured by the rational-experiential inventory (REI) and the cognitive reflection test. At the end, participants answered a series of demographic questions.

The collected data were then analysed by regression analysis. Based on the findings of this study, the rational thinking measured by the REI40 has a significant and positive effect on the performance of individuals in conflict management with difficult personalities in an organisational context; in other words, rational thinking leads to better performance in conflict management than experiential thinking.

The value of this article lies in the direct study of the impact of thinking styles on conflict management, which was done by focusing on difficult organisational personalities. Also, using gamification in research design is another research initiative.

Considering the transition of communicational channels from physical to digital spaces, this study aims to provide a theoretical foundation for understanding engagement in electronic word of mouth (eWoM) among managers and customers in the hospitality and tourism industry.

This study uses the four aggregate dimensions, namely, performance expectancy, efforts expectancy, social influence and facilitations condition. Further, this paper uses the 14 second-order themes of the Unified Theory of Acceptance and Use of Technology with a data set that represents elements that can trigger eWoM, both from managers’ and customers’ perspectives. The process of data structuration follows thematic analysis and axial coding techniques.

The results of this study show that performance expectancy, facilitation conditions, social influence and effort expectancy all trigger positive eWoM generation in the hospitality and tourism industry indicating customers’ and managers’ perspectives.

This novel study provides a theoretical foundation and novel propositions for future research work on the role of novel antecedents that can trigger eWoM in the hospitality and tourism industry. This paper also provides a benchmark for practitioners and policymakers in their strategic decisions-making towards improving business performance through positive eWoM.