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Previous studies have rarely integrated the financing modes of a capital-constrained manufacturer with the choices of online sales strategies. To address this gap, the authors study how a manufacturer selects optimal financing modes under different sales strategies in three dual-channel supply chains.

This paper considers three sales strategies, namely, combining a traditional retailer channel with one of the direct selling, reselling and agency selling channels, and two common financing modes, namely, bank financing and retailer financing. The authors obtain equilibrium outcomes of the manufacturer and traditional retailer and then provide the conditions for them to select optimal financing modes under three sales strategies.

The results indicate that the manufacturer’s financing decisions rely on the initial capital and interest rates, and the manufacturer selects retailer financing only if the initial capital is relatively larger. In terms of financing mode options, the retailer financing mode is more beneficial for the manufacturer under the three sales strategies. From the perspective of sales strategies, the direct selling model is more beneficial. In addition, the higher the consumer acceptance of the online channel, the more profits the manufacturer obtains.

This paper provides suggestions on how the capital-constrained manufacturer chooses financing modes and sales strategies.

This paper integrates the financing mode and different sales strategies to investigate the manufacturer’s optimal operational decisions. These sales strategies allow us to investigate the manufacturer’s optimal financing modes in the presence of both different financing modes and sales strategies.

While the phenomenon of technostress has received significant attention from researchers in recent years, empirical findings concerning the consequences of specific forms of techno-stressors have remained scattered and contradictory. The authors aim to integrate the conclusions of previous studies to understand the effects of specific techno-stressors on strain and job performance.

This study employs meta-analytic techniques to calibrate the findings of 67 studies investigating more than 63,100 employees.

In general, not all techno-stressors have adverse effects. In particular, techno-uncertainty does not impact job performance. In addition, relative weight analyses reveal the relative importance of techno-complexity and techno-insecurity as predictors of both strain and job performance. Finally, this study finds that the effects of specific techno-stressors on job performance vary depending on research participants' gender, educational attainment and employment status.

First, this study provides a more nuanced view of the effects of specific techno-stressors. Second, this research clarifies the relative importance of specific techno-stressors as predictors of strain and job performance. Finally, this study reveals the moderating effects of demographic variables on the relationships between specific techno-stressors and job performance.

Drawing on the transactional model of stress, this study develops a theoretical model to understand the lurking behavior on enterprise social media (ESM).

Based on a questionnaire survey, this research study collected valid data from 301 ESM users in the workplace. Hierarchical regression analyses are used to analyze the conceptual framework.

The results show that Zhongyong thinking is a significant predictor of lurking and emotional exhaustion and that lurking can cause emotional exhaustion. Lurking mediates the relationship between Zhongyong thinking and emotional exhaustion. Additionally, communication overload moderates the relationship between lurking and emotional exhaustion as well as the indirect relationship between Zhongyong thinking and emotional exhaustion through lurking.

This article examines the antecedents of lurking by considering Zhongyong thinking and explores how lurking on ESM influences emotional exhaustion. This research contributes to the literature on techno-stressors, lurking and ESM and hopefully contributes to the growing dialog about the consequences of lurking in the workplace.

Despite consumers' widespread use of social media platforms, there is scant research on the underlying factors that influence their willingness to share digital footprints on social media. The purpose of this study is to address this research gap by examining consumers' cognitive and affective attitudes simultaneously.

This research used quantitative method by using online survey administered to a sample of 733 social media users.

The findings indicate both cognitive and affective attitudes jointly influence consumers' behavioural intentions with trust as a key construct mediating the relationship between attitudinal antecedents and consumers' willingness to share digital footprints on social media.

This study contributes to the information systems (IS) literature by offering a comprehensive framework constituting the joint attitudinal components as antecedents to consumers' behavioural intention for sharing digital footprints while trust works as a mediator.

This paper has important managerial implications. It helps marketers and IS managers in profiling consumers, understanding consumption patterns, sharing of digital footprints, which are useful for effective market segmentation, product development and future design of social media platforms. It informs social media providers of the importance of not only focussing on functional aspects but also underscores the essence of paying attention to consumers' affect towards social media platforms, especially trust.

The paper presents an original framework that explains the influence of joint attitudinal components on behavioural intention, with trust as a mediator.

1. To improve the causality analysis performance, a novel causality detector based on time-delayed convergent cross mapping (TD-CCM) is proposed in this work. 2. Identify the root cause of plant-wide oscillations in process control system.

A novel causality analysis framework is proposed based on denoising and periodicity-removing TD-CCM (time-delayed convergent cross mapping). We first point out that noise and periodicity have adverse effects on causality detection. Then, the empirical mode decomposition (EMD) and detrended fluctuation analysis (FDA) are combined to achieve denoising. The periodicities are effectively removed through singular spectrum analysis (SSA). Following, the TD-CCM can accurately capture the causalities and locate the root cause by analyzing the filtered signals.

1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. Simulation studies show that the proposed method is able to improve the causality analysis performance. 3. Industrial case study shows the proposed method can be used to analyze the root cause of plant-wide oscillations in process control system.

1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. The influences of noise and periodicity on causality analysis are investigated. 3. Simulations and industrial case shows that the proposed method can improve the causality analysis performance and can be used to identify the root cause of plant-wide oscillations in process control system.

The US Department of Agriculture (USDA), Soil Conservation Services (SCS) rainfall-runoff model has been applied worldwide since 1954 and adopted by Malaysian government agencies. Malaysia does not have regional specific curve numbers (CN) available for the use in rainfall-runoff modelling, and therefore a SCS-CN practitioner has no option but to adopt its guideline and handbook values which are specific to the US region. The selection of CN to represent a watershed becomes subjective and even inconsistent to represent similar land cover area. In recent decades, hydrologists argue about the accuracy of the predicted runoff results from the model and challenge the validity of the key parameter, initial abstraction ratio coefficient (λ) and the use of CN. Unlike the conventional SCS-CN technique, the proposed calibration methodology in this chapter discarded the use of CN as input to the SCS model and derived statistically significant CN value of a specific region through rainfall-runoff events directly under the guide of inferential statistics. Between July and October of 2004, the derived λ was 0.015, while λ = 0.20 was rejected at alpha = 0.01 level at Melana watershed in Johor, Malaysia. Optimum CN of 88.9 was derived from the 99% confidence interval range from 87.4 to 96.6 at Melana watershed. Residual sum of square (RSS) was reduced by 79% while the runoff model of Nash–Sutcliffe was improved by 233%. The SCS rainfall-runoff model can be calibrated quickly to address urban runoff prediction challenge under rapid land use and land cover changes.

A major challenge faced by MNCs is how to manage knowledge transfer between headquarters and subsidiaries located in dissimilar cultural contexts. While the impact of culture on knowledge transfer has been widely acknowledged, an important gap in the literature is how cultural distance (external variable) and subsidiary roles (internal variable) interact in impacting on knowledge flows in MNCs. The purpose of this paper is to present an integrative model to advance understanding of the interaction between cultural distance and subsidiary roles in the knowledge transfer process within MNCs.

An environment‐strategy‐performance framework is developed based on a review of extant literature. A case study is used to substantiate the framework. It was conducted in an MNC in China and involved in‐depth interviews with expatriate and local managers.

Knowledge transfer in MNCs is influenced by external context (cultural distance) and internal mechanisms (subsidiary roles). The direction and magnitude of knowledge flows is related to the strategic roles of the subsidiary and is influenced by cultural distance. Negative impacts are evident where cultural distance is large, with positive impacts where cultural synergies occur. Changes in Chinese cultural values impacting on knowledge transfer are also evident.

The conceptual model combines internal resource and external environmental perspectives. It links literature on cross‐cultural management, subsidiary roles and MNC knowledge transfer. Furthermore, it explores linkages between culture and knowledge transfer, cultural distance and subsidiary roles, knowledge transfer and subsidiary roles. The model is operationalised in a China‐based case study.

The purpose of this study is to explore a signal processing method to improve the angular rate accuracy of micro-electro-mechanical system (MEMS) gyroscope by combining numerous gyroscopes.

To improve the dynamic performance of the signal processing method, the interacting multiple model (IMM) can be applied to the fusion of gyroscope array. However, the standard IMM has constant Markov parameter, which may reduce the model switching speed. To overcome this problem, an adaptive IMM filter is developed based on the kurtosis of the gyroscope output, in which the transition probabilities are adjusted online by utilizing the dynamic information of the rate signal.

The experimental results indicate that the precision of the gyroscope array composed of six gyroscopes increases significantly and the kurtosis-based adaptive Markov parameter IMM filter (K-IMM) performs better than the baseline methods, especially under dynamic conditions. These experiments prove the validity of the proposed fusion method.

The proposed method can improve the accuracy of MEMS gyroscopes without breakthrough on hardware, which is necessary to extend their utility while not restricting the overwhelming advantages.

A K-IMM algorithm is proposed in this paper, which is used to improve the angular rate accuracy of MEMS gyroscope by combining numerous gyroscopes.

Given the cryptocurrency market boom in recent years, this study aims to identify the factors influencing cryptocurrency pricing and the major gaps for future research.

A systematic literature review was undertaken. Three databases, Scopus, Web of Science and EBSCOhost, were used for this review. The final analysis comprised 88 articles that met the eligibility criteria.

The influential factors were identified and categorized as supply and demand, technology, economics, market volatility, investors’ attributes and social media. This review provides a comprehensive and consolidated view of cryptocurrency pricing and maps the significant influential factors.

This paper is the first to systematically and comprehensively review the relevant literature on cryptocurrency to identify the factors of pricing fluctuation. This research contributes to cryptocurrency research as well as to consumer behaviors and marketing discipline in broad.