Despite extensive research on the role of managers in digital transformation, there is limited investigation into their readiness for organizational change. This knowledge gap is particularly significant because managers' belief in digital readiness is a crucial prerequisite for effectively guiding and implementing digital transformation initiatives. Therefore, this study aims to explore the influence of managerial digital readiness on their intention to implement digital transformation. To achieve this, we propose an integrated model that combines the digital readiness framework with the theory of planned behaviour, consisting of three sub-constructs: digital structure readiness, digital technology readiness, and digital strategy readiness. Our findings reveal that all these sub-constructs positively impact managers' intention to implement digital transformation. This contributes to the micro-foundations of digital transformation, specifically highlighting the pivotal role of managers.

The current literature is becoming increasingly interested in employee management behavior. In this article, we focus on how leader narcissism affects employees' knowledge management behavior, including knowledge sharing, knowledge hiding, and knowledge manipulation. The questionnaire was administered to 260 employees in mainland China to test our hypothesis. Results showed that leader narcissism is significantly related to employee knowledge management behaviors. Specifically, leader narcissism is negatively related to employee knowledge sharing, and is positively related to employee knowledge hiding and manipulating. Employee trust in management mediates these relationships, and power distance orientation moderates the relationship between leader narcissism and employees' trust in management. Our study discusses theoretical and practical implications.

The purpose of this paper is to propose a research model to investigate the relationships between dynamic capabilities, business model innovation, and business performance. The moderating role of digital transformation in the research model is also examined. A total of 262 managers in Chinese small and medium-sized enterprises (SMEs) were enrolled via an online questionnaire. Data is scrutinized by partial least square structural equation modeling (PLS-SEM) using SmartPLS 3.0. The findings of this study reveal that dynamic capabilities can positively affect business model innovation and business performance. Furthermore, business model innovation partially mediates the relationship between dynamic capabilities and business performance. Meanwhile, digital transformation has a positive moderating role in the aforementioned relationships. These findings bridge the research gaps in extant literature and contribute to theoretical and practical implications for SMEs.

Accurate side-channel attacks can non-invasively or semi-invasively extract secure information from hardware devices using "side- channel" measurements. The thermal profile of an IC is one class of side channel that can be used to exploit the security weaknesses in a design. Measurement of junction temperature from an on-chip thermal sensor or top metal layer temperature using an infrared thermal image of an IC with the package being removed can disclose secret keys of a cryptographic design through correlation power analysis. In order to identify the design vulnerabilities to thermal side channel attacks, design time simulation tools are highly important. However, simulation of thermal side-channel emission is highly complex and computationally intensive due to the scale of simulation vectors required and the multi-physics simulation models involved. Hence, in this paper, we have proposed a fast and comprehensive Machine Learning (ML) augmented thermal simulation methodology for thermal Side-Channel emission Analysis (SCeA). We have developed an innovative tile-based Delta-T Predictor using a data-driven DNN-based thermal solver. The developed tile based Delta-T Predictor temperature is used to perform the thermal side-channel analysis which models the scenario of thermal attacks with the measurement of junction temperature. This method can be 100-1000x faster depending on the size of the chip compared to traditional FEM-based thermal solvers with the same level of accuracy. Furthermore, this simulation allows for the determination of location- dependent wire temperature on the top metal layer to validate the scenario of thermal attack with top metal layer temperature. We have demonstrated the leakage of the encryption key in an 128-bit AES chip using both proposed tile-based temperature calculations and top metal wire temperature calculations, quantified by simulation MTD (Measurements-to-Disclosure).

Transportation is undergoing significant change due to the growth of ride-sharing, electric cars, car-sharing, and self-driving cars. Organizations that have significant real-estate dedicated to on-premise employee car parking are therefore looking to adapt the use of this space, motivated by the opportunity to become greener, improve sharing, and pursue new revenue opportunities. In this paper, we outline our experiences from instrumenting, measuring, and analyzing car-park usage in our University's multi-storey parking lot, and building a model that explores its use for multiple purposes in the near future. Our specific contributions are as follows: (1)We begin by describing experiences and challenges in measuring car-park usage on our campus and cleaning the collected data; (2)We analyze data collected over 23 weeks (covering teaching and non-teaching periods)and draw insights into the usage patterns, including occupancy patterns by times-of-day and days-of-week, and identifying various user groups based on attributes such as arrival time and duration of stay; (3)We develop a queuing model to optimize the use of parking space for generating revenue from shared cars with minimal impact on private car users. We believe our study guides campus managers wanting to generate more value from their existing parking resources.

We present a scalable Bayesian framework for low-rank decomposition of multiway tensor data with missing observations. The key issue of pre-specifying the rank of the decomposition is sidestepped in a principled manner using a multiplicative gamma process prior. Both continuous and binary data can be analyzed under the framework, in a coherent way using fully conjugate Bayesian analysis. In particular, the analysis in the non-conjugate binary case is facilitated via the use of the Pólya-Gamma sampling strategy which elicits closed-form Gibbs sampling updates. The resulting samplers are efficient and enable us to apply our framework to large-scale problems, with time-complexity that is linear in the number of observed entries in the tensor. This is especially attractive in analyzing very large but sparsely observed tensors with very few known entries. Moreover, our method admits easy extension to the supervised setting where entities in one or more tensor modes have labels. Our method outperforms several state-of-the-art tensor decomposition methods on various synthetic and benchmark real-world datasets.