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To achieve digital transformation, organizations have continued to rely on integrating the capabilities of information technology (IT) to facilitate decision-making and developing their reconfiguration capability to enhance agile operations. The pressure imposed by digital transformation necessitates investigations on leveraging different IT capabilities to attain substantial organizational agility in an optimal configuration. This study aims to provide a new perspective on balancing IT structural capabilities and proposes a framework for evaluating their coalignment and complementary returns based on resource orchestration theory.

A multi-method approach is used to evaluate the research model. This study tests hypotheses and explores the potential coalignment and complementary returns of balance in structural models and response surface analysis. Then, it analyzes the qualitative data and provides complementary findings to corroborate and confirm complex relationships.

Balanced structural IT capabilities facilitate organizational agility but cooperate differently with internal (e.g. IT proactive stance) and external (e.g. environmental volatility) environmental factors. Balance between IT integration and reconfiguration must be maintained from several approaches during search/selection and configuration/deployment.

This study theorizes and empirically investigates the interactive mechanisms of two IT capabilities in influencing organizational agility under different boundary conditions. It enriches the understanding of balancing capabilities for organizational agility in digital transformation.

Coal is a critical global energy source, and fluctuations in its price significantly impact related enterprises' profitability. This study aims to develop a robust model for predicting the coal price index to enhance coal purchase strategies for coal-consuming enterprises and provide crucial information for global carbon emission reduction.

The proposed coal price forecasting system combines data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. It addresses the challenge of merging low-resolution and high-resolution data by adaptively combining both types of data and filling in missing gaps through interpolation for internal missing data and self-supervision for initiate/terminal missing data. The system employs self-supervised learning to complete the filling of complex missing data.

The ensemble model, which combines long short-term memory, XGBoost and support vector regression, demonstrated the best prediction performance among the tested models. It exhibited superior accuracy and stability across multiple indices in two datasets, namely the Bohai-Rim steam-coal price index and coal daily settlement price.

The proposed coal price forecasting system stands out as it integrates data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. Moreover, the system pioneers the use of self-supervised learning for filling in complex missing data, contributing to its originality and effectiveness.

Mobile location-based service (m-LBS) seems like a new class of personalized service due to location positioning technologies. This work aims to investigate consumer readiness (RED) toward m-LBS based on integrating pull effect- and push effect-related factors into the technology acceptance model (TAM).

An online survey collected data from 423 participants, and the research framework was analyzed using structural equation modeling (SEM).

The results divulge that consumer RED is determined by TAM antecedents, including usefulness (USE) and ease of use (EOU). EOU motivates USE in m-LBS. Regarding pull effect-related factors, absorptive capacity (ABC) is the strongest positive factor influencing consumer RED to use m-LBS, followed by technology willingness (TWI) and innovativeness (INN). Moreover, INN, trust (TRU) and perceived risk (RIS) significantly influence USE and EOU.

This work endeavors to explicate customer RED toward m-LBS by incorporating some meaningful pull effect-related dimensions (i.e. ABC, TWI and INN) and pushing effect-related dimensions (i.e. RIS) into crucial antecedents rooted in TAM. Thus, the findings assist practitioners in developing marketing strategies by boosting pull effects and controlling push effects on customer engagement in m-LBS.