A decision support model for buying battery electric vehicles considering consumer learning and psychological behavior

Developments in battery electric vehicles (BEVs) have received more and more attentions in the last decades due to alleviating carbon emissions and energy crisis. Consequently, how to rank alternative BEVs to assist consumers make better purchasing decisions is a worthy research study. However, there are still some defects in the existing studies for ranking of BEVs: 1) the evaluation index system of BEVs is not comprehensive; 2) the determination of criteria weights cannot be well applied to the actual purchase scenarios; and 3) the psychological behavior of consumers is ignored. To address those shortcomings, this paper proposes a decision support model to assist with consumers to buy BEVs. First, a systematic evaluation criteria system of BEVs including quantitative and qualitative indicators from parameter configurations and online reviews is constructed. Then, a weight algorithm considering consumer learning is proposed to determine the criteria weights. Furthermore, a decision support process considering consumers' regret avoidance behavior is proposed. Finally, an actual BEV purchase case is given to illustrate the practicability of the decision support model. This can be seen in case studies the proposed support model can be well applied to consumers with different regret avoidance behaviours.     

Support for the underdog brand biography: Effects on consumer attitude and behavior

Conflict between favorites and underdogs is an everyday phenomenon. Research suggests that people support the underdog as they see a reflection of their own self in the person at a disadvantaged position. Two studies examine the effect of underdog brand biography in two different contexts. Study 1 provides support for the notion that underdog brand biographies for established brands elicit stronger consumer response than underdog brand biography for new brands. In study 2, a 2 × 2 between-subjects experimental design measured respondents’ reaction toward an underdog brand biography over a top dog brand biography under conditions of public versus private consumption. Results demonstrate that consumers exposed to underdog brand biographies in the private consumption condition will be more likely to respond favorably than when they are exposed to the public consumption condition. Based on the results it is suggested that advertisers make use of underdog brand biographies in case of established brands. Furthermore, choice of underdog brand narratives is likely to bring positive results under conditions of private consumption.     

The angel investment decision: insights from Australian business angels

While much has been written about the investment criteria of business angels, few studies explore why these particular criteria are important to them. The Australian context has a diverse range of actors along with complex jurisdictional arrangements, making for an interesting background for investigation of the angel finance phenomenon. We examine 12 business angels in the rapidly changing Australian context and use nine corroborating participants to validate responses and identify four key drivers – personal experience, trust, the need to contribute and realistic expectations – that influence business angels during the initial investment process.     

Using machine learning and big data for efficient forecasting of hotel booking cancellations

Cancellations are a key aspect of hotel revenue management because of their impact on room reservation systems. In fact, very little is known about the reasons that lead customers to cancel, or how it can be avoided. The aim of this paper is to propose a means of enabling the forecasting of hotel booking cancellations using only 13 independent variables, a reduced number in comparison with related research in the area, which in addition coincide with those that are most often requested by customers when they place a reservation. For this matter, machine-learning techniques, among other artificial neural networks optimised with genetic algorithms were applied achieving a cancellation rate of up to 98%. The proposed methodology allows us not only to know about cancellation rates, but also to identify which customer is likely to cancel. This approach would mean organisations could strengthen their action protocols regarding tourist arrivals.     

国土空间用途的权衡决策方法研究

研究目的:探索国土空间用途冲突协调路径,为中国"多规合一"及国土空间规划提供理论与方法指导。研究方法:在分析冲突根源的基础上建立国土空间权衡决策模型,构建"战略理性+技术理性+沟通理性"的国土空间权衡决策模式,并以丰县为例开展实证研究。研究结果:(1)国土空间利用的冲突本质上是利益相关者之间的利益冲突,只有利益权衡,没有利益优化;(2)国土空间权衡决策准则、国土空间价值评估、价值权衡和沟通定权共同组成国土空间权衡决策模式;(3)实证研究表明:经济发展战略下丰县镇村、农业、生态空间占比分别为18.51%、74.20%、7.29%,粮食安全战略下占比分别为16.07%、76.66%、7.27%,生态文明战略下占比分别为17.88%、69.06%、13.06%,权衡结果较合理。研究结论:国土空间权衡决策受国家战略驱动、以沟通为基础、以技术为手段,可有效解决国土空间用途冲突问题。     

Misclassification errors in labor force statuses and the early identification of economic recessions

Accurate identification of economic recessions in a timely fashion is a major macroeconomic challenge. The so-called Sahm recession indicator (Sahm, 2019), one of the most reliable early detectors of the U.S. recessions, relies on changes in unemployment rates, and is thus subject to misclassification errors in labor force statuses based on survey data. We propose a novel misclassification-errors correction to improve the predictive timeliness and provide a proper threshold value. Using historical data, we show that the adjusted unemployment-based recession indicator offers earlier identification of economic recessions.     

How justified is abandoning money in the conduct of monetary policy in Australia on the grounds of instability in the money‐demand function?

The role of money in the design and conduct of monetary policy has reemerged as an important issue in both advanced and developing economies, especially since the 2007 global financial crisis. A growing body of recent literature suggests that the causal relationship between money supply growth and inflation remains intact across countries and over time and that this relation is not conditional on the stability of the money‐demand function or whether money is endogenous or exogenous. Moreover, critical for a rule‐based monetary policy is the presence of a long‐run stable money‐demand function, rather than a short‐run money‐demand model that may exhibit instability for many reasons, including problems with estimating a money‐demand model with high‐frequency data. Provided that a stable money‐demand function exists, it could be useful to establish long‐run equilibrium relations among money, output, prices, and exchange rates, as the classical monetary theory suggests. Within this analytical framework, this paper addresses the question of whether money has any role in the conduct of monetary policy in Australia. The conventional wisdom is that the money‐demand function in Australia has been unstable since the mid‐1980s due to financial deregulation and reforms; this led to a change in the strategy of monetary policy for price stability in the form of inflation targeting that ignores money insofar as inflation and its control are concerned. This paper reports empirical findings for Australia, obtained from a longer quarterly data series over the period 1960Q1–2015Q1, which suggest that instability in the narrow‐money‐demand function in Australia was primarily due to the exclusion of variables which have become important in the deregulated environment since the 1980s. These findings are confirmed by an expanded form of the narrow‐money‐demand function that was found stable over the past two decades, although it experienced multiple structural breaks over the study period. The paper draws the conclusion that abandoning the monetary aggregate as an instrument of monetary policy in Australia, under a rule‐based monetary policy such as inflation targeting, cannot be justified by instability in the money‐demand function or even by lack of a causal link between money supply growth and inflation.     

Information asymmetry in the innovation adoption decision of tourism and hospitality SMEs in emerging markets: A mixed-method analysis

Investment decisions, particularly innovation adoption decisions, can sustain long-term viability of small and medium enterprises (SMEs). However, information asymmetry could limit the ability of SME management to make informed innovation adoption decisions. Even though information asymmetry is a multidimensional construct, prior literature has only focused on financial information asymmetry in such decisions. Therefore, the current study fills the research gap by conducting two separate studies: one analyzes World Bank Group data from 1250 observations in 11 emerging markets, and the other interviews eight SME owners and managers. The findings of the mixed-method studies suggest that financial, business regulation, and court information asymmetry decrease the likelihood of SMEs' product innovation adoption. The study contributes to a more comprehensive understanding of information asymmetry in SMEs’ decision-making rooted in agency theory. Moreover, its methodological contribution lies in measuring information asymmetry with micro-level data.     

The puzzle of greenhouse gas footprints of oil abundance

The present inquiry lays a groundwork for the analysis of the net greenhouse gas (GHG) footprint of oil in the oil-abundant settings. To address the research question, the study puts forward a three-sector decision model, which provides a common ground for the assessment of the interaction of the structuralist and institutional factors influencing environmental pollution in the oil-reliant economies. The study shows that fossil-fuel abundance triggers forces, which induce diametrically opposed effects concerning atmospheric pollution. These are the rising carbon-intensive oil extraction and processing and fossil-fueled power generation versus shrinkage of the carbon-intensive manufacturing and growth of the low-carbon tertiarization. The theoretical analysis enables compartmentalization of the essential factors, which determine GHG emissions in the respective countries. To assess the significance of the proposed theoretical framework, the study employs multivariate panel co-integration techniques and two-stage fixed effects estimations for a dataset of 38 oil-producing countries for the time period between 1960 and 2018. In contrast to the existing literature, this study drives apart from the black box approaches that employ just one omnibus variable, per capita income.     

Two-period supply chain coordination strategies with ambidextrous sustainable innovations

This study considers a manufacturer with ambidextrous sustainable innovation capability selling products in environmentally conscious market through an independent retailer in a two-period game setting. We design a two-period game theoretic and dyadic supply chain (SC) model considering exploitative and exploratory nature of environmental innovations. We study five different contract types, namely, wholesale price contract, vertical Nash game structure, cost sharing contract, revenue sharing contract and two-part tariff contract. We demonstrate the impact of market sensitivity towards sustainable innovation and cost parameters on optimal level of decision parameters. The equilibrium results reveal that a suitably designed two-part tariff contract can be used to achieve coordination in a fragmented SC. The equilibrium results assist managers to optimise the SC based on the two-period contract model. The results obtained in this study can help the decision-makers to take decisions on investment in the ambidextrous sustainable innovation under different types of contract structures.