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Digital technologies are essential for improving efficiency and unlocking new opportunities in various domains. The purpose of this study is to assess whether digital technologies can ameliorate servitization among manufacturing firms via the interaction of organizational slack and research and development (R&D) intensity.

Drawing on resource-based and service-dominant logic, the study employs a deductive approach and gathers empirical evidence from 1,929 listed A-shares manufacturing firms in the top-seven China mainland industrial provinces spanning the period 2012–2021. It used fixed-effect logistic regression techniques while controlling for various factors to analyze the relationship between digital technologies and manufacturing firm servitization.

The findings revealed that digital technologies significantly ameliorate manufacturing firms' servitization. Moreover, the study uncovers the contingent nature of this relationship, demonstrating that high levels of both internal and external slack, which provide flexibility and support, intensify the direction of digital technologies towards servitization. Additionally, R&D intensity reflects the firm's commitment to innovation, thereby enhancing synergistic effects in the relationship.

This study contributes robust and comprehensive empirical evidence that validates and establishes a clear baseline relationship reflecting the most current digital technology landscape and its implications for manufacturing firms servitization. Moreover, it provides a more patterned understanding of how internal and external slack typologies and R&D intensity contextualize our study’s findings. Additionally, it demonstrates how our theoretical synthesis advances firms’ strategic shifts towards service-oriented business models through digital technologies.

Emerging market multinationals often face a variety of legitimacy challenges as they engage in cross-border acquisitions in developed countries, which requires an assortment of legitimacy strategies best aligned with the legitimacy challenges they face. This study advocates for a configurational perspective that examines how different configurations of legitimacy challenges, organizational characteristics, and legitimacy strategies influence the likelihood of deal completion in cross-border acquisitions by emerging market multinational enterprises (EMNEs).

Based on 328 cross-border acquisition cases by Chinese firms, this study adopts the fuzzy-set qualitative comparative analysis to examine the combined effects of institutional distance, political affinity, equity sought, architecture design, sensitive·industry and state-owned and enterprise (SOE) on cross-border acquisition completion.

This study identifies six pathways with different configurations for deal completion, suggesting that a deal's overall legitimacy falls at the intersection of the country-level institution and the firm-level characters and strategy evaluations.

This study investigates how nested legitimacy influences cross-border acquisition completion by offering a holistic and configurational understanding of the deal completion of cross-border acquisitions by EMNEs and yields useful insights for future research on cross-border acquisition completion and legitimacy.

This study aims to explore the traditional plant dyeing of Xinjiang Atlas silk fabrics, providing references for the comprehensive utilization of plant dyes in intangible cultural heritage.

The focus of this study is on dyeing experiments of Atlas silk fabrics using safflower extracts, constrained by regional resources. Safflower dry flowers grown in Xinjiang were selected, rinsed with pure water and rubbed. Yellow pigments were removed by adding edible white vinegar. Red pigments from safflower were extracted using an alkaline solution prepared with Populus euphratica ash, a special product of Xinjiang. The extraction rate was analyzed under varying material-to-liquor ratios, pH values, times and temperatures. Direct dyeing process experiments were conducted to obtain different colorimetric L, a, b and K/S values for comparison. Samples with good color development were selected to test the impact of dyeing immersions on color development, and their color fastness, UV protection and antibacterial effects were verified.

The dyeing experiments on silk fabrics confirmed their UV protection capabilities and antibacterial properties, demonstrating effectiveness against E. coli and Staphylococcus aureus. As a major producer of safflower, Xinjiang underscores the significance of safflower as an essential plant dyes on the Silk Road. This study reveals its market potential and suitability for use in the plant dyeing process of Atlas silk, producing vibrant red and pink colors.

The experiments indicated that after removing yellow pigments, the highest extraction rate of red pigment from safflower was achieved at a pH value of 10–11, a temperature of 30°C and an extraction time of 40 min. The best bright red color effect with strong color fastness was obtained with a material-to-liquor ratio of 1:20, a temperature of 40°C and three immersions. The best light pink color effect with strong color fastness was a material-to-liquor ratio of 1:80, a temperature of 30°C and two immersions.

Scholars and practitioners alike are paying attention to entrepreneurial orientation (EO) as an antecedent of the financial performance of SMEs. Other factors foster and improve SMEs' financial performance. This paper aims to shed the light on other two different strategic orientations that may help enhance SMEs' financial performance in addition to EO, namely; market orientation (MO) and brand orientation (BO).

The three different important strategic orientations are explored through two different studies. The first study was conducted to determine the different effects of the three orientations on SMEs' financial performance. Data were collected using a questionnaire among a convenient sample (131) of business owners/managers, and next PLS-SEM was used for data analysis. The financial performance of firms in the second study is hypothesized to be an outcome of a combination of different strategic orientations; therefore, the fsQCA method is applied to explore the causal recipes of those orientations.

The paper concluded that the three different strategic orientations are collectively, of paramount importance to strategic managers of SMEs.

The brand, market and EOs have been discussed discretely in previous studies and this study attempted to provide managers/owners of SMEs with a holistic view of the three different orientations and the amalgamation among them to be beneficial for better financial performance.

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

Traditional grey models are integer order whitening differential models; these models are relatively effective for the prediction of regular raw data, but the prediction error of irregular series or shock series is large, and the prediction effect is not ideal.

The new model realizes the dynamic expansion and optimization of the grey Bernoulli model. Meanwhile, it also enhances the variability and self-adaptability of the model structure. And nonlinear parameters are computed by the particle swarm optimization (PSO) algorithm.

Establishing a prediction model based on the raw data from the last six years, it is verified that the prediction performance of the new model is far superior to other mainstream grey prediction models, especially for irregular sequences and oscillating sequences. Ultimately, forecasting models are constructed to calculate various energy consumption aspects in Chongqing. The findings of this study offer a valuable reference for the government in shaping energy consumption policies and optimizing the energy structure.

It is imperative to recognize its inherent limitations. Firstly, the fractional differential order of the model is restricted to 0 < a < 2, encompassing only a three-parameter model. Future investigations could delve into the development of a multi-parameter model applicable when a = 2. Secondly, this paper exclusively focuses on the model itself, neglecting the consideration of raw data preprocessing, such as smoothing operators, buffer operators and background values. Incorporating these factors could significantly enhance the model’s effectiveness, particularly in the context of medium-term or long-term predictions.

This contribution plays a constructive role in expanding the model repertoire of the grey prediction model. The utilization of the developed model for predicting total energy consumption, coal consumption, natural gas consumption, oil consumption and other energy sources from 2021 to 2022 validates the efficacy and feasibility of the innovative model.

These findings, in turn, provide valuable guidance and decision-making support for both the Chinese Government and the Chongqing Government in optimizing energy structure and formulating effective energy policies.

This research holds significant importance in enriching the theoretical framework of the grey prediction model.

The highlights of the paper are as follows:

1. A novel grey Bernoulli prediction model is proposed to improve the model’s structure.

2. Fractional derivative, fractional accumulating generation operator and Bernoulli equation are added to the new model.

3. The proposed model can achieve full compatibility with the traditional mainstream grey prediction models.

4. Energy consumption in Chongqing verifies that the performance of the new model is much better than that of the traditional grey models.

5. The research provides a reference basis for the government to formulate energy consumption policies and optimize energy structure.

A novel grey Bernoulli prediction model is proposed to improve the model’s structure.

Fractional derivative, fractional accumulating generation operator and Bernoulli equation are added to the new model.

The proposed model can achieve full compatibility with the traditional mainstream grey prediction models.

Energy consumption in Chongqing verifies that the performance of the new model is much better than that of the traditional grey models.

The research provides a reference basis for the government to formulate energy consumption policies and optimize energy structure.

This study aims to clarify influencing factors of overcapacity of new energy enterprises in China and accurately predict whether these enterprises have overcapacity.

Based on relevant data including the experience and evidence from the capital market in China, the research establishes a generic univariate selection-comparative machine learning model to study relevant factors that affect overcapacity of new energy enterprises from five dimensions. These include the governmental intervention, market demand, corporate finance, corporate governance and corporate decision. Moreover, the bridging approach is used to strengthen findings from quantitative studies via the results from qualitative studies.

The authors' results show that the overcapacity of new energy enterprises in China is brought out by the combined effect of governmental intervention corporate governance and corporate decision. Governmental interventions increase the overcapacity risk of new energy enterprises mainly by distorting investment behaviors of enterprises. Corporate decision and corporate governance factors affect the overcapacity mainly by regulating the degree of overconfidence of the management team and the agency cost. Among the eight comparable integrated models, generic univariate selection-bagging exhibits the optimal comprehensive generalization performance and its area under the receiver operating characteristic curve Area under curve (AUC) accuracy precision and recall are 0.719, 0.960, 0.975 and 0.983, respectively.

The proposed integrated model analyzes causes and predicts presence of overcapacity of new energy enterprises to help governments to formulate appropriate strategies to deal with overcapacity and new energy enterprises to optimize resource allocation. Ten main features which affect the overcapacity of new energy enterprises in China are identified through generic univariate selection model. Through the bridging approach, the impact of the main features on the overcapacity of new energy enterprises and the mechanism of the influence are analyzed.

Conflicts frequently occur in construction projects. Matching appropriate contractual and relational governance with conflict features to promote cooperation and thus minimize the negative influences of conflict is an issue that deserves attention. Our study classifies conflict types into task, process and relationship conflict and defines their combinations as conflict profiles. By conceptualizing contractual governance as the complexity of contract provisions and the strictness of contract enforcement and relational governance as trust, our study aims to explore the configurational impacts of conflict profiles and these governance mechanisms on parties’ cooperative behaviors.

A questionnaire survey was conducted, and 238 valid questionnaires were received. Fuzzy set qualitative comparative analysis was performed.

Four configurations produce cooperative behaviors. The combined use of detailed contracts, rigid enforcement and high trust enhances cooperation and such a governance arrangement is not subject to any conflict profile. A relatively low level of conflict requires detailed contracts and high levels of trust. For the conflict profile characterized by high task and process conflict and low relationship conflict, parties can select contractual governance-dominant or relational governance-dominant approaches.

Theoretically, our study reveals the matching relationships between conflict profiles and governance mechanisms, enriching the research on conflict profiles in construction projects and the interrelation between contractual and relational governance. Practically, the findings provide project managers guidance for conflict management and selecting governance mechanisms.

The purpose of this study is to examine the effect of collaborative innovation networks on new product development (NPD) performance in small and medium-sized enterprises (SMEs). It also investigates the mediating role of business model innovation and moderating role of collaboration experience and external information technology (IT) capability in the above relationship.

To test the research hypotheses about the relationships above, survey data were collected from 209 Chinese manufacturing SMEs. Multiple hierarchical regressions analysis was used to examine the hypotheses.

Results reveal that collaborative innovation networks have positive impacts on NPD performance in SMEs. Moreover, business model innovation mediates and collaboration experience and external IT capability positively moderate the relationship between collaborative innovation networks and NPD performance in SMEs.

This study paints a more complete picture of the relationship between collaborative innovation networks and NPD performance in SMEs and advances the understanding of how and when SMEs enhance their NPD performance through collaborative innovation networks.

This study aims to determine the distinctive attributes of pet-friendly hotels, examine the importance versus the performance of each attribute, and test the effect of the performance of attributes on customer behavior.

This research applied qualitative and quantitative methods to explore the unique attributes of pet-friendly hotels. The importance-performance analysis was used to investigate how customers perceive the importance and the performance of each attribute, and regression analysis was used to examine how the performance of attributes influences customer behavior.

This study identified six distinctive attributes of pet-friendly hotels. Furthermore, the findings underlined the significance of pet-related activity support for pet-friendly hotel selections, and addressed an urgent need for the improvement of pet-friendly safety measures. Last, results showed the salient effect of pet-related activity support on visit and word-of-mouth intentions, and the strong influence of pet-centric facilities and services on willingness to pay premiums for pet-friendly hotels.

The findings provided useful insights into how hotel companies could effectively position themselves as pet-friendly from customers’ perspectives.

This research is among the first to determine distinctive attributes and adopt importance-performance analysis in pet-friendly hotel context.