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Existing studies have paid less attention to the impact of knowledge accumulation on digital transformation and its boundary conditions. Hence, this study aims to investigate the effects of ambidextrous knowledge accumulation on manufacturing digital transformation under the moderation of dynamic capability.

This study divides knowledge accumulation into exploratory and exploitative knowledge accumulation and divides dynamic capability into alliance management capability and new product development capability. To clarify the relationship among ambidextrous knowledge accumulation, dynamic capability and manufacturing digital transformation, the authors collect data from 421 Chinese listed manufacturing enterprises from 2016 to 2020 and perform analysis by multiple hierarchical regression method, heterogeneity test and robustness analysis.

The empirical results show that both exploratory and exploitative knowledge accumulation can significantly promote manufacturing digital transformation. Keeping ambidextrous knowledge accumulation in parallel is more conducive than keeping single-dimensional knowledge accumulation. Besides, dynamic capability positively moderates the relationship between ambidextrous knowledge accumulation and manufacturing digital transformation. Moreover, the heterogeneity test shows that the impact of ambidextrous knowledge accumulation and dynamic capabilities on manufacturing digital transformation varies widely across different industry segments or different regions.

First, this paper shifts attention to the role of ambidextrous knowledge accumulation in manufacturing digital transformation and expands the connotation and extension of knowledge accumulation. Second, this study reveals that dynamic capability is a vital driver of digital transformation, which corroborates the previous findings of dynamic capability as an important driver and contributes to enriching the knowledge management literature. Third, this paper provides a comprehensive micro measurement of ambidextrous knowledge accumulation and digital transformation based on the development characteristics of the digital economy era, which provides a theoretical basis for subsequent research.

This paper aims to examine the effects of managerial ability (MA) on the likelihood and the timeliness of goodwill impairment and explore whether the desirable effect of MA vary with the degree of agency problems.

The authors propose a unified framework to simultaneously examine the effects of MA on the likelihood and the timeliness of goodwill impairment by incorporating a market-based impairment indicator (denoted as BTM), MA and the interaction of BTM with MA to this study’s regression model to account for the likelihood of goodwill impairment. BTM addresses the timeliness of goodwill impairment.

This study finds that firms with higher MA have lower likelihood of goodwill impairment, and such firms are more likely to recognize goodwill impairment in a timely manner when the underlying value of goodwill is economically impaired. This desirable effect of MA is more pronounced in non-state-owned enterprise (SOEs) and firms without chief executive officer (CEO) duality.

Firms can reduce the losses arising from goodwill impairment by enhancing the ability of their management teams combined with improved corporate governance structure.

This paper provides novel insights on understanding the role of MA in not only reducing the likelihood but also enhancing the timeliness of goodwill impairment. The findings help advance the upper echelons theory by uncovering the heterogenous effects of executives with different levels of ability.

The purpose of this study is to examine different paths to overcoming the liability of foreignness. Based on the eclectic paradigm, the authors construct a theoretical framework comprising enterprise nature, location choice, entry mode and internationalization strategy.

The paper uses fuzzy-set qualitative comparative analysis (fsQCA) method to test the framework with data covering 120 multinational Chinese subsidiaries in 34 host in 2019.

The results show that liability of foreignness (LOF) is multiple concurrency, equifinality and asymmetry. When investing in Belt and Road (B&R) countries, non-SEOs can weaken LOF by applying the greenfield mode and resource-seeking strategy, other MNEs can implement a market- or resource-seeking strategy via cross-border M&A to reduce LOF. But when investing in non-B&R countries with a strategic asset-seeking strategy, the LOF is increased. The B&R initiative can reduce the LOF effectively.

The authors construct a general framework to explain the paths of overcoming LOF by bridging the OLI with LOF and introduce fsQCA method into the field of LOF to make up for the shortcoming of existing test method by explaining the influence of more than three factors on LOF.

The purpose of this paper is to present an integrated data-driven framework for processing and analyzing large-scale vehicle maintenance records to get more comprehensive understanding on vehicle quality.

We propose a framework for vehicle quality analysis based on maintenance record mining and Bayesian Network. It includes the development of a comprehensive dictionary for efficient classification of maintenance items, and the establishment of a Bayesian Network model for vehicle quality evaluation. The vehicle design parameters, price and performance of functional systems are modeled as node variables in the Bayesian Network. Bayesian Network reasoning is then used to analyze the influence of these nodes on vehicle quality and their respective importance.

A case study using the maintenance records of 74 sport utility vehicle (SUV) models is presented to demonstrate the validity of the proposed framework. Our results reveal that factors such as vehicle size, chassis issues and engine displacement, can affect the chance of vehicle failures and accidents. The influence of factors such as price and performance of engine and chassis show explicit regional differences.

Previous research usually focuses on limited maintenance records from a single vehicle producer, while our proposed framework enables efficient and systematic processing of larger-scale maintenance records for vehicle quality analysis, which can support auto companies, consumers and regulators to make better decisions in purchase choice-making, vehicle design and market regulation.