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Individuals’ driving behavior data are becoming available widely through Global Positioning System devices and on-board diagnostic systems. The incoming data can be sampled at rates ranging from one Hertz (or even lower) to hundreds of Hertz. Failing to capture substantial changes in vehicle movements over time by “undersampling” can cause loss of information and misinterpretations of the data, but “oversampling” can waste storage and processing resources. The purpose of this study is to empirically explore how micro-driving decisions to maintain speed, accelerate or decelerate, can be best captured, without substantial loss of information.

This study creates a set of indicators to quantify the magnitude of information loss (MIL). Each indicator is calculated as a percentage to index the extent of information loss (EIL) in different situations. An overall information loss index named EIL is created to combine the MIL indicators. Data from a driving simulator study collected at 20 Hertz are analyzed (N = 718,481 data points from 35,924 s of driving tests). The study quantifies the relationship between information loss indicators and sampling rates.

The results show that marginally more information is lost as data are sampled down from 20 to 0.5 Hz, but the relationship is not linear. With four indicators of MILs, the overall EIL is 3.85 per cent for 1-Hz sampling rate driving behavior data. If sampling rates are higher than 2 Hz, all MILs are under 5 per cent for importation loss.

This study contributes by developing a framework for quantifying the relationship between sampling rates, and information loss and depending on the objective of their study, researchers can choose the appropriate sampling rate necessary to get the right amount of accuracy.

Basic safety message (BSM) is a core subset of standard protocols for connected vehicle system to transmit related safety information via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Although some safety prototypes of connected vehicle have been proposed with effective strategies, few of them are fully evaluated in terms of the significance of BSM messages on performance of safety applications when in emergency.

To address this problem, a data fusion method is proposed to capture the vehicle crash risk by extracting critical information from raw BSMs data, such as driver volition, vehicle speed, hard accelerations and braking. Thereafter, a classification model based on information-entropy and variable precision rough set (VPRS) is used for assessing the instantaneous driving safety by fusing the BSMs data from field test, and predicting the vehicle crash risk level with the driver emergency maneuvers in the next short term.

The findings and implications are discussed for developing an improved warning and driving assistant system by using BSMs messages.

The findings of this study are relevant to incorporation of alerts, warnings and control assists in V2V applications of connected vehicles. Such applications can help drivers identify situations where surrounding drivers are volatile, and they may avoid dangers by taking defensive actions.

A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour. Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy. Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration. A single-step application of machine learning (ML) is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy. The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.

This research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars. The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data, and the second step detects abnormal fuel economy in relation to contextual information. Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model. The contextual anomaly is detected by following two approaches, kernel quantile estimator and one-class support vector machine. The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour. Any error beyond a threshold is classified as an anomaly. The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection. The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder, and the performance of both models is compared. The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.

A composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder. Both models achieve prediction accuracy within a range of 98%–100% for prediction as a first step. Recall and accuracy metrics for anomaly detection using kernel quantile estimator remains within 98%–100%, whereas the one-class support vector machine approach performs within the range of 99.3%–100%.

The proposed approach does not consider socio-demographics or physiological information of drivers due to privacy concerns. However, it can be extended to correlate driver's physiological state such as fatigue, sleep and stress to correlate with driving behaviour and fuel economy. The anomaly detection approach here is limited to providing feedback to driver, it can be extended to give contextual feedback to the steering controller or throttle controller. In the future, a controller-based system can be associated with an anomaly detection approach to control the acceleration and braking action of the driver.

The suggested approach is helpful in monitoring and reinforcing fuel-economical driving behaviour among fleet drivers as per different environmental contexts. It can also be used as a training tool for improving driving efficiency for new drivers. It keeps drivers engaged positively by issuing a relevant warning for significant contextual anomalies and avoids issuing a warning for minor operational errors.

This paper contributes to the existing literature by providing an ML pipeline approach to track and monitor instantaneous fuel economy rather than relying on average fuel economy values. The approach is further extended to detect contextual driving behaviour anomalies and optimises fuel economy. The main contributions for this approach are as follows: (1) a prediction model is applied to fine-grained time series driving data to predict instantaneous fuel consumption. (2) Anomalous fuel economy is detected by comparing prediction error against a threshold and analysing error patterns based on contextual information.

Scholars have not agreed with each other on how people would behave after experiencing a catastrophic event. They could save more as a precautionary action for future difficulties or save less with a carpe diem attitude. This study aims to attempt to shed light on this debate with empirical observations on how the Covid-19 pandemic has affected household saving decisions.

The two waves of the survey data allowed us to investigate both instantaneous and ongoing effects of Covid-19 on household saving decisions. The instantaneous effect refers to the immediate impact of the crisis, while the ongoing effect refers to the lasting impact of the pandemic when economic recovery had started. The variation in the number of confirmed cases across cities during the two waves provides the source of power for identification. The authors extend their analyses of the impact of Covid-19 on the household saving decision by using ordinary least squares models. Due to the ordered nature of survey responses, the authors also rerun all baseline models using the ordered probit regression method.

This paper studied the impact of the Covid-19 pandemic on household saving decisions in China. This study found that households in the most affected cities would save more during the Covid-19 but tend to save less when the disaster started fading away. Combining findings in Kun et al. (2013) and Filipski et al. (2015), people do become more pessimistic during and after the Covid-19, possibly driving their observed precautionary and cape diem behaviors during the two points of time. Heterogeneity analysis shows that specific households would dramatically change their saving behavior. These observations might be useful for policymakers who concern the economic recovery after this pandemic disaster.

Understanding how the Covid-19 pandemic would affect household consumption vs saving decisions is important for the economic recovery after this disaster comes to an end. The analyses presented in this research could be useful for policymakers who concern appropriate policies aiming to boost consumption and economic activities after Covid.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.

Aims to explain the driving forces for networked inventory management. Discusses major developments with respect to customer requirements, networked organizations and networked inventory management. Presents high level specifications of networked inventory management information systems (NIMISs). Reviews some decision systems for inventory management, and compares traditional inventory management to networked inventory management. Uses these insights to outline NIMISs for several types of inventory management decision systems. Summarizes the results of the study, and provides an outlook on further research.

The purpose of this paper is to examine intentional herding among institutional investors with a particular focus on the technology sector that was the driver of the “New Economy” in the USA during the dot-com bubble of the 1990s.

Using data on technology stockholdings of 115 large institutional investors, the authors test the presence of herding by examining linear dependence and feedback between individual investors’ technology stockholdings and that of the aggregate market. Unlike other models to detect herding, the authors use Geweke (1982) type causality tests that allow authors to disentangle spurious herding from intentional herding via tests of bidirectional and instantaneous causality across portfolio positions in technology stocks.

After controlling information-based (spurious) herding, the tests show that 38 percent of large institutional investors tend to intentionally herd in technology stocks.

The findings support the existing literature that investment decisions by large institutional investors are not only driven by fundamental information, but also by cognitive bias that is characterized by intentional herding.

Develops an original 12‐step management of technology protocol and applies it to 51 applications which range from Du Pont’s failure in Nylon to the Single Online Trade Exchange for Auto Parts procurement by GM, Ford, Daimler‐Chrysler and Renault‐Nissan. Provides many case studies with regards to the adoption of technology and describes seven chief technology officer characteristics. Discusses common errors when companies invest in technology and considers the probabilities of success. Provides 175 questions and answers to reinforce the concepts introduced. States that this substantial journal is aimed primarily at the present and potential chief technology officer to assist their survival and success in national and international markets.

The purpose of this study is to analyse various risks associated with third-party logistics (3PL) in the coffee supply chain and to present a framework that computes the influence of these risks on the critical success factors of the coffee supply chain.

The risks have been identified through a comprehensive literature review and validation by industry experts. The paper utilises an interpretive structural modelling (ISM) methodology for developing a hierarchical relationship among the CSFs. Furthermore, fuzzy MICMAC analysis is carried out to categorise these CSFs based on their driving power and dependence value. The fuzzy technique for order preferences by the similarity of an ideal solution (fuzzy-TOPSIS) approach has been applied to prioritise the risks associated with 3PL based on their ability to influence the CSFs of the coffee SC. Furthermore, we performed a sensitivity analysis to analyse the stability of the results obtained in this study.

This study illustrates ten risks associated with 3PL and five CSFs in the coffee supply chain. The analysis revealed that coffee enterprises need to develop a balanced pricing strategy to ensure a sustainable competitive advantage, whereas the lack of direct customer communication is the most dominant 3PL risk affecting the CSFs.

This research provides coffee enterprises with a generalised framework with set parameters that can be used to attain a successful coffee supply chain in any developing nation.

The study contributes to the literature by being the first kind of study, which has used fuzzy ISM-MICMAC to analyse the CSFs of the coffee supply chain and fuzzy-TOPSIS for analysing the impact of various risks associated with the 3PL in the coffee supply chain. Thus, this work can be considered a benchmark for future research and advancement in the coffee business field.