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The development of communication and artificial intelligence technologies has raised interest in connectivity and increased autonomy of automated earthmoving equipment for earthwork. These changes are motivating work to reduce uncertainties, in terms of improving equipment object detection capability and reducing strikes and accidents on site. The purpose of this study is to illustrate industrial drivers for automated earthwork systems; identify the specific capabilities which make the transformation happen; and finally determine use cases that create value for the system. These three objectives act as components of a technology roadmap for automated and connected earthwork and can guide development of new products and services.

This paper used a text mining approach in which the required data was captured through a structured literature review, and then expert knowledge was used for verification of the results.

Automated and connected earthwork can enhance construction site and its embraced infrastructure, resilience by avoiding human faults during operations. Automating the monitoring process can lead to reliable anticipation of problems and facilitate real-time responses to unexpected situation via connectedness capabilities. Research findings are presented in three sections: industrial perspectives, trends and drivers for automated and connected earthwork; capabilities which are met by technologies; and use cases to demonstrate different capabilities.

This study combines the results of disintegrated and fragmented research in the area of automated and connected earthwork and categorises them under new capability levels. The identified capabilities are classified in three main categories including reliable environmental perception, single equipment decision-making toward safe outcomes and fleet-level safety enhancement. Finally, four different levels of automation are proposed for earthwork technology roadmap.

A successful process of design concept evaluation has positive influence on subsequent processes. This study aims to consider the evaluation information at multiple stages and the interaction among evaluators and improve the credibility of evaluation results.

This paper proposes a multi-stage approach for design concept evaluation based on complex network and bounded confidence. First, a network is constructed according to the evaluation data. Depending on the consensus degree of evaluation opinions, the number of evaluation rounds is determined. Then, bounded confidence rules are applied for the modification of preference information. Last, a planning function is constructed to calculate the weight of each stage and aggregate information at multiple evaluation stages.

The results indicate that the opinions of the evaluators tend to be consistent after multiple stages of interactive adjustment, and the ordering of design concept alternatives tends to be stable with the progress of the evaluation.

Updating preferences according to the bounded confidence rules, only the opinions within the trust threshold are considered. The attribute information of the node itself is inadequately considered.

This method addresses the need for considering the evaluation information at each stage and minimizes the impact of disagreements within the evaluation group on the evaluation results.

The aim of this study is to propose an efficient rule extraction and integration approach for identifying phishing websites. The proposed approach can elucidate patterns of phishing websites and identify them accurately.

Hyperlink indicators along with URL-based features are used to build the identification model. In the proposed approach, very simple rules are first extracted based on individual features to provide meaningful and easy-to-understand rules. Then, the F-measure score is used to select high-quality rules for identifying phishing websites. To construct a reliable and promising phishing website identification model, the selected rules are integrated using a simple neural network model.

Experiments conducted using self-collected and benchmark data sets show that the proposed approach outperforms 16 commonly used classifiers (including seven non–rule-based and four rule-based classifiers as well as five deep learning models) in terms of interpretability and identification performance.

Investigating patterns of phishing websites based on hyperlink indicators using the efficient rule-based approach is innovative. It is not only helpful for identifying phishing websites, but also beneficial for extracting simple and understandable rules.

Malicious web domain identification is of significant importance to the security protection of internet users. With online credibility and performance data, the purpose of this paper to investigate the use of machine learning techniques for malicious web domain identification by considering the class imbalance issue (i.e. there are more benign web domains than malicious ones).

The authors propose an integrated resampling approach to handle class imbalance by combining the synthetic minority oversampling technique (SMOTE) and particle swarm optimisation (PSO), a population-based meta-heuristic algorithm. The authors use the SMOTE for oversampling and PSO for undersampling.

By applying eight well-known machine learning classifiers, the proposed integrated resampling approach is comprehensively examined using several imbalanced web domain data sets with different imbalance ratios. Compared to five other well-known resampling approaches, experimental results confirm that the proposed approach is highly effective.

This study not only inspires the practical use of online credibility and performance data for identifying malicious web domains but also provides an effective resampling approach for handling the class imbalance issue in the area of malicious web domain identification.

Online credibility and performance data are applied to build malicious web domain identification models using machine learning techniques. An integrated resampling approach is proposed to address the class imbalance issue. The performance of the proposed approach is confirmed based on real-world data sets with different imbalance ratios.

Despite the conceptual, empirical and theoretical advances in alignment–performance relationship, there is a limited research on the alignment dimensions and organizational performance measures. Though strategic alignment is believed to improve organizational performance, the purpose of this paper is to develop conjectures for understanding how different alignment dimensions influence organizational performance measures.

The data were acquired from 161 senior IT and business managers paired responses in China and were analyzed by using a structural equation modeling technique.

The hypothesized relationships are largely supported. Thus, quality-oriented strategic alignment dimension has a significant relationship with all performance measures. Contrary to expectations, both product and marketing-oriented strategic alignment dimensions do not show a significant impact on financial return. The marketing-oriented strategic alignment dimension also has an insignificant relationship with operational excellence.

This study suggests that the business–IT alignment can be dimensioned to better combine business strategy and IT strategy. Hence, managers can focus specific alignment dimension instead of entire strategies of a firm for a better decision making.

Findings suggest guidance for formulating combined business and IT strategic alignment into dimensions and proposing insightful and practical implications.

The multifaceted effect of IT in organizations has been widely examined. However, the intervening role of IT strategy and business strategy on the effect of managing IT on firm performance remains less strong. This study examines how managing information technology (MIT) effects on firm performance by looking at the mediating role of IT strategy and business strategy.

Drawing on the resource-based view of IT and contingency perspective, theoretical insights for managing IT and the mediating effect of IT strategy and business strategy on firm performance are established. The model is empirically tested by using hierarchical regression and structural equation modeling for the data collected through the survey of 194 senior IT and business managers in China.

The significant and impactful relationship found in the model for the proposed idea. The results show that both IT strategy and business strategy partially mediate the effect of managing IT on firm performance.

The findings highlight that managing IT does not merely influence better firm performance; instead, the coherent amalgamation of IT strategy and business strategy can enrich firm performance. The theoretical and practical implications are also discussed.

In line with the call for rigorous research to integrate the managing IT and firm strategies, this study demonstrates the mediating role of business strategy and IT strategy between the managing IT and the firm performance relationship, hence contributing to the IS research literature.

Though prior studies have attempted to explore the various effects of managing information technology (IT) investment on firm performance, the mechanism through which management of IT impact on firm performance rests less clear. The purpose of this study is to examine the impact of managing IT and business-IT alignment on firm performance.

Drawing on the resource-based theory and process theory, this study examines how managing IT impacts business-IT alignment and firm performance. The primary survey of 182 responses from IT and business managers from Sri Lanka was empirically examined.

The findings reveal that managing IT has a positive and strong impact on business-IT alignment and firm performance. Further, business-IT alignment partially mediates between managing IT investment and firm performance relationships.

Today, businesses have invested a massive amount of money in IT investment, and the return on this investment is always a serious concern for managers and industry practitioners. This study finding proposes meaningful insights on managing IT, business-IT alignment and firm performance.

This study opens up the black box on the above nomological linkage and contributes to the literature by extending the theoretical lenses while suggesting insightful and practical implications.

Despite the widespread use of univariate empirical mode decomposition (EMD) in financial market forecasting, the application of multivariate empirical mode decomposition (MEMD) has not been fully investigated. The purpose of this study is to forecast the stock price index more accurately, relying on the capability of MEMD in modeling the dependency between relevant variables.

Quantitative and comprehensive assessments were carried out to compare the performance of some selected models. Data for the assessments were collected from three major stock exchanges, namely, the standard and poor 500 index from the USA, the Hang Seng index from Hong Kong and the Shanghai Stock Exchange composite index from China. MEMD-based support vector regression (SVR) was used as the modeling framework, where MEMD was first introduced to simultaneously decompose the relevant covariates, including the opening price, the highest price, the lowest price, the closing price and the trading volume of a stock price index. Then, SVR was used to set up forecasting models for each component decomposed and another SVR model was used to generate the final forecast based on the forecasts of each component. This paper named this the MEMD-SVR-SVR model.

The results show that the MEMD-based modeling framework outperforms other selected competing models. As per the models using MEMD, the MEMD-SVR-SVR model excels in terms of prediction accuracy across the various data sets.

This research extends the literature of EMD-based univariate models by considering the scenario of multiple variables for improving forecasting accuracy and simplifying computability, which contributes to the analytics pool for the financial analysis community.

Despite the enormous potential of mobile health (mHealth), identifying the asymmetric relationship among the predictors towards intention to use (ITU) of mHealth tends to remain unresolved. This study aims to investigate the predictors and their asymmetric effects on ITU of mHealth through patients and healthcare professionals.

An integrated information systems (IS) model with four additional constructs has been developed to analyze symmetric and asymmetric effects on ITU of mHealth. An exploratory survey on 452 mHealth users with prior experience was conducted to evaluate the model using a mixed-method approach including partial least squares-based structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) technique.

The findings show that facilitating conditions, personal awareness building, perceived enjoyment, effort expectancy and perceived usefulness have predictive power for ITU of mHealth. In contrast, fsQCA reveals four more alternative solutions, including the main drivers explored by PLS-SEM. The results indicate that various conditions that were not crucial in PLS-SEM analysis are shown to be sufficient conditions in fsQCA.

This study contributes to theory by integrating self-actualization factors (i.e. personal awareness building, patients as decision support unit) into the IS model. And practically, this study makes an essential contribution to users' ITU of mHealth, enabling relevant stakeholders to build strategies to implement mHealth successfully.

While mHealth has revolutionized healthcare and the prior literature only showed linear relationships, this empirical study revealed asymmetrical relationships among the determinants of ITU of mHealth. Thus, this study extends to the growing body of literature on the use of mHealth technology in the least developing nation.