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The effective prediction of crude oil futures prices can provide a reference for relevant enterprises to make production plans and investment decisions. To the nonlinearity, high volatility and uncertainty of the crude oil futures price, a matrixed nonlinear exponential grey Bernoulli model combined with an exponential accumulation generating operator (MNEGBM(1,1)) is proposed in this paper.

First, the original sequence is processed by the exponential accumulation generating operator to weaken its volatility. The nonlinear grey Bernoulli and exponential function models are combined to fit the preprocessed sequence. Then, the parameters in MNEGBM(1,1) are matrixed, so the ternary interval number sequence can be modeled directly. Marine Predators Algorithm (MPA) is chosen to optimize the nonlinear parameters. Finally, the Cramer rule is used to derive the time recursive formula.

The predictive effectiveness of the proposed model is verified by comparing it with five comparison models. Crude oil futures prices in Cushing, OK are predicted and analyzed from 2023/07 to 2023/12. The prediction results show it will gradually decrease over the next six months.

Crude oil futures prices are highly volatile in the short term. The use of grey model for short-term prediction is valuable for research. For the data characteristics of crude oil futures price, this study first proposes an improved model for interval number prediction of crude oil futures prices.

We take Cumulative Prospect Theory (CPT) seriously by rigorously estimating structural models using the full set of CPT parameters. Much of the literature only estimates a subset of CPT parameters, or more simply assumes CPT parameter values from prior studies. Our data are from laboratory experiments with undergraduate students and MBA students facing substantial real incentives and losses. We also estimate structural models from Expected Utility Theory (EUT), Dual Theory (DT), Rank-Dependent Utility (RDU), and Disappointment Aversion (DA) for comparison. Our major finding is that a majority of individuals in our sample locally asset integrate. That is, they see a loss frame for what it is, a frame, and behave as if they evaluate the net payment rather than the gross loss when one is presented to them. This finding is devastating to the direct application of CPT to these data for those subjects. Support for CPT is greater when losses are covered out of an earned endowment rather than house money, but RDU is still the best single characterization of individual and pooled choices. Defenders of the CPT model claim, correctly, that the CPT model exists “because the data says it should.” In other words, the CPT model was borne from a wide range of stylized facts culled from parts of the cognitive psychology literature. If one is to take the CPT model seriously and rigorously then it needs to do a much better job of explaining the data than we see here.

This paper aims to identify the income and price elasticities of demand for residential electricity in Latin America and the Caribbean (LAC) and to verify their main determinants.

Meta-analysis and meta-regression methods were applied. After collecting and filtering journal articles, the authors obtained a sample composed of 76 studies covering 1979–2020.

The results show that the LAC's income elasticity is approximately 0.20 and 0.92 for the short and long term, respectively. The LAC's price elasticity is approximately −0.37 and −0.46 for the short and long term, respectively. Furthermore, the estimates are affected by the data structure, the estimation method used and the sampling period.

The authors close a gap in the literature by analyzing the price and income elasticities of demand through meta-analysis and meta-regression.

This study aims to examine how entrepreneurship education influences intentions for starting a technology venture among science, technology, engineering and mathematics (STEM) students with particular attention to gender differences. This study builds on the model of entrepreneurial event and social role theory to assess the impact of entrepreneurship education on feasibility, desirability and intentions for technology entrepreneurship.

The hypotheses are tested with a sample of 879 Bulgarian science and engineering students from 15 universities. To test the models, this study uses ordinary least squares and logistic regressions with robust standard errors and Hayes mediation analysis with bootstrap bias-corrected confidence interval estimations for indirect effects. Two-stage Heckman regressions to control for sample selection bias and other robustness checks including propensity score matching were used.

Results show that entrepreneurship education, measured as participation in an entrepreneurship course, has a stronger impact on feasibility, desirability and intentions for technology entrepreneurship for female STEM students compared to their male counterparts. As such, this study supports the notion that entrepreneurship education could be part of a solution to counteract societal norms that position technology entrepreneurship as a less desirable and/or less feasible choice for women in STEM. However, attention should be paid to the operationalization of entrepreneurship education, as other measures of entrepreneurship education (role models, entrepreneurship education support) did not have a moderation effect with gender.

The authors assume a positive correlation between entrepreneurial intentions and entrepreneurial behavior. Future studies should include actual entrepreneurial behavior to paint a more complete picture of the effect of entrepreneurship education.

Little is known about the role of entrepreneurship education in the field of technology entrepreneurship, and even less about the potential gender differences in entrepreneurship education among STEM students. The study contributes to the literature by examining factors that could help close the persistent gender gap in technology entrepreneurship.

Measuring the diffusion of protests, or more generally, the diffusion of events, is an ongoing task in social sciences. This paper proposes an inter-event approach to study what types of protests tend to diffuse or decline. We develop a standardized, five-step procedure to measure what we define as “event diffusion momentum” (EDM): (1) employ event-based data containing information on the time, location, and features of each protest; (2) define the temporal and spatial ranges of interest; (3) for each observation, count the number of events before and after it within the defined ranges; (4) predict the numbers of post-event and pre-event protests with appropriate count models; (5) calculate the ratios of predicted values for each predictor and confidence intervals using the delta method. The ratio is the EDM. Applying this method to Dynamics of Collective Action (DoCA) data, we identify several micro- and macro-level factors associated with protest diffusion in the United States, 1960–1995. We conclude with the implications and generalizability of the proposed method.

The overall goal of science is to build a valid and reliable body of knowledge about the functioning of the world and how applying that knowledge can change it. As personnel and human resources management researchers, we aim to contribute to the respective bodies of knowledge to provide both employers and employees with a workable foundation to help with those problems they are confronted with. However, what research on research has consistently demonstrated is that the scientific endeavor possesses existential issues including a substantial lack of (a) solid theory, (b) replicability, (c) reproducibility, (d) proper and generalizable samples, (e) sufficient quality control (i.e., peer review), (f) robust and trustworthy statistical results, (g) availability of research, and (h) sufficient practical implications. In this chapter, we first sing a song of sorrow regarding the current state of the social sciences in general and personnel and human resources management specifically. Then, we investigate potential grievances that might have led to it (i.e., questionable research practices, misplaced incentives), only to end with a verse of hope by outlining an avenue for betterment (i.e., open science and policy changes at multiple levels).

Auxetics are the class of cellular materials with a negative Poisson’s ratio. This paper aims to study the low-cost 3D printing capabilities and printing variations and improve the indentation performance of the re-entrant diamond auxetic metamaterial by tuning the structural parameters that have not been reported.

The design of experiment strategy was adopted to study the influence of re-entrant angle, diamond angle and thickness-to-length ratio on relative density, load, stiffness and specific energy absorption (SEA) during indentation experimentally. Grey relational analysis was chosen as a multi-objective optimisation technique to optimise structural performance. Surrogate models were proposed to uphold the metamaterial’s tailorability for desired application needs. The fit and efficacy of the proposed models were tested using specific statistical techniques. The predominant deformation mechanisms observed with the alteration in structural parameters were discussed.

The improvements noticed are 48 times hike in load, 112 times improvement in stiffness and 10 times increase in SEA for optimised structures. The surrogate models are proven to predict the outputs accurately for new input parameters. In-situ displacement fields are visualised with an image processing technique.

To the best of the authors’ knowledge, the indentation performance of the re-entrant diamond auxetic metamaterials has not been reported and reported for the first time. The influence of geometrical parameters on the newly developed structure under concentrated loading was evaluated. The geometry-dependent printing variations associated with 3D printing have been discussed to help the user to fabricate re-entrant diamond auxetic metamaterial.

The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.

The purpose of this paper is to investigate and quantify the impact of product complexity, including architectural complexity, on operator learning, productivity and quality performance in both assembly and disassembly operations. This topic has not been extensively investigated in previous research.

An extensive experimental campaign involving 84 operators was conducted to repeatedly assemble and disassemble six different products of varying complexity to construct productivity and quality learning curves. Data from the experiment were analysed using statistical methods.

The human learning factor of productivity increases superlinearly with the increasing architectural complexity of products, i.e. from centralised to distributed architectures, both in assembly and disassembly, regardless of the level of overall product complexity. On the other hand, the human learning factor of quality performance decreases superlinearly as the architectural complexity of products increases. The intrinsic characteristics of product architecture are the reasons for this difference in learning factor.

The results of the study suggest that considering product complexity, particularly architectural complexity, in the design and planning of manufacturing processes can optimise operator learning, productivity and quality performance, and inform decisions about improving manufacturing operations.

While previous research has focussed on the effects of complexity on process time and defect generation, this study is amongst the first to investigate and quantify the effects of product complexity, including architectural complexity, on operator learning using an extensive experimental campaign.