The relationship between attribute performance and customer satisfaction: an interpretable machine learning approach

Understanding the relationship between attribute performance(AP)and customer satisfaction(CS)is crucial for the hospitality industry.However,accurately modeling this relationship remains challenging.To address this issue,we propose an interpretable machine learning-based dynamic asymmetric analysis(IML-DAA)approach that leverages interpretable machine learning(IML)to improve traditional relationship analysis methods.The IML-DAA employs extreme gradient boosting(XGBoost)and SHapley Additive exPlanations(SHAP)to construct relationships and explain the significance of each attribute.Following this,an improved version of penalty-reward contrast analysis(PRCA)is used to classify attributes,whereas asymmetric impact-performance analysis(AIPA)is employed to determine the attribute improvement priority order.A total of 29,724 user ratings in New York City collected from TripAdvisor were investigated.The results suggest that IML-DAA can effectively capture non-linear relationships and that there is a dynamic asymmetric effect between AP and CS,as identified by the dynamic AIPA model.This study enhances our understanding of the relationship between AP and CS and contributes to the literature on the hotel service industry.

How time-inconsistent preferences influence venture capital exit decisions?A new perspective for grandstanding

Considering that the assumption of time consistency does not adequately reveal the mechanisms of exit decisions of venture capital(VC),this study proposes two kinds of time-inconsistent preferences(i.e.,time-flow inconsistency and time-point incon-sistency)to advance research in this field.Time-flow inconsistency is in line with the previous time inconsistency literature,while time-point inconsistency is rooted in the VC fund’s finite lifespan.Based on the assumption about the strategies guiding future behaviors,we consider four types of venture capitalists:time-consistent,time-point-inconsistent,naïve,and sophisticated venture capitalists,of which the latter three are time-inconsistent.We derive and compare the exit thresholds of these four types of venture capitalists.The main results include:(1)time-inconsistent preferences acceler-ate the exits of venture capitalists;(2)the closer the VC funds expiry dates are,t`he more likely time-inconsistent venture capitalists are to accelerate their exits;and(3)future selves caused by time-flow inconsistency weaken the effect of time-point inconsist-ency.Our study provides a behavioral explanation for the empirical fact of young VCs’grandstanding.

New developments in wind energy forecasting with artificial intelligence and big data:a scientometric insight

Accurate forecasting results are crucial for increasing energy efficiency and lowering energy consumption in wind energy.Big data and artificial intelligence(AI)have great potential in wind energy forecasting.Although the literature on this subject is extensive,it lacks a comprehensive research status survey.In identifying the evolution rules of big data and AI methods in wind energy forecasting,this paper summarizes the studies on big data and AI in wind energy forecasting over the last two decades.The existing big data types,analysis techniques,and forecasting methods are classified and sorted by combining literature reviews and scientometrics methods.Furthermore,the research trend of wind energy forecasting methods is determined based on big data and artificial intelligence by combing the existing research hotspots and frontier progress.Finally,this paper summarizes existing research’s opportunities,challenges,and implications from various perspectives.The research results serve as a foundation for future research and promote the further development of wind energy forecasting.

Tourism demand forecasting and tourists’search behavior:evidence from segmented Baidu search volume

Given the importance of web search volume for reflecting tourists'preferences for certain tourism services and destinations,incorporating these data into forecasting models can significantly improve forecasting performance.This study enriches the literature on tourism demand forecasting and tourists'search behavior through segmented Baidu search volume data.First,this study divides Baidu search volume data based on volume sources and periods.Then,by analyzing the most relevant keywords in tourism demand in different segments,this study captures the dynamic characteristics of tourist search behavior.Finally,this study adopts a series of econometric and machine learning models to further improve the performance of tourism demand and forecasting.The findings indicate that tourists’search behavior has changed significantly with the prevalence and popularization of 4G technology and suggest that search volume improves forecasting performance,especially search volume on mobile terminals,from 2014M1–2019M12.

Nutrient and growth elucidation of a novel coated urea on oilseed rape in three main cultivation areas

A novel coated urea(MVCU)was prepared,and its application effect was verified by field trials of oilseed rape in three main cultivation areas.Meanwhile,the nutrient release and coating layer changes of MVCU in static water at 25
C and different soils were systematically evaluated.MVCU showed a long nutrient release time under static water(77 days)and soil incubation(140 days)conditions due to the slow degradation of the coating layer in MVCU,and its nitrogen release coincided well with oilseed rape nitrogen demand.The above results were further confirmed by FT-IR spectra and SEM analysis.Compared with conventional urea(U),the field trials of MVCU in the three main cultivation areas showed high nitrogen utilization efficiency and yield advantages in oilseed rape.The field trials results indicated that the MVCU significantly enhanced the aboveground dry matter(28.7%),the seed nitrogen concentration(9.5%)and aboveground nitrogen accumulation(42.5%)of oilseed rape at the mature stage as compared to that of the U.The oilseed rape yield enhanced by 932.8 kg/hm^(2),the average growth rate was 65.1%,and nitrogen utilization efficiency increased by 21.2%.In short,MVCU has the advantages of excellent slow-release performance and strong applicability,and its yield-increasing effect on oilseed rape could reach or even be better than that of traditional fertilization.

A decomposition clustering ensemble learning approach for forecasting foreign exchange rates

A decomposition clustering ensemble(DCE)learning approach is proposed for forecasting foreign exchange rates by integrating the variational mode decomposition(VMD),the selforganizing map(SOM)network,and the kemel extreme leaming machine(KELM).First,the exchange rate time series is decomposed into N subcomponents by the VMD method.Second,each subcomponent series is modeled by the KELM.Third,the SOM neural network is introduced to cluster the subcomponent forecasting results of the in-sample dataset to obtain cluster centers.Finally,each cluster's ensemble weight is estimated by another KELM,and the final forecasting results are obtained by the corresponding clusters'ensemble weights.The empirical results illustrate that our proposed DCE learning approach can significantly improve forecasting performance,and statistically outperform some other benchmark models in directional and level forecasting accuracy.

A KELM-Based Ensemble Learning Approach for Exchange Rate Forecasting

In this paper, a KELM-based ensemble learning approach, integrating Granger causality test, grey relational analysis and KELM(Kernel Extreme Learning Machine), is proposed for the exchange rate forecasting. The study uses a set of sixteen macroeconomic variables including, import,export, foreign exchange reserves, etc. Furthermore, the selected variables are ranked and then three of them, which have the highest degrees of relevance with the exchange rate, are filtered out by Granger causality test and the grey relational analysis, to represent the domestic situation. Then, based on the domestic situation, KELM is utilized for medium-term RMB/USD forecasting. The empirical results show that the proposed KELM-based ensemble learning approach outperforms all other benchmark models in different forecasting horizons, which implies that the KELM-based ensemble learning approach is a powerful learning approach for exchange rates forecasting.