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Facing the increasing competition and uncertainty, when and how to improve service innovation performance with the help of digital business strategy has become an important issue for global service firms. In this study, organizational memory level and dispersion are regarded as moderating variables and market intelligence response is introduced as a mediator, aiming at clarifying the boundary conditions and mechanism of digital business strategy affecting service innovation performance.

A survey was conducted among middle and senior managers from 245 service firms in China. The data were analyzed using SPSS and Mplus software for reliability and validity analysis, hypothesis testing and robustness testing.

Digital business strategy was positively related to the service innovation performance of service firms. Market intelligence responsiveness mediated the positive effect of digital business strategy on service innovation performance of service firms. The positive effect between digital business strategy and market intelligence responsiveness was strengthened when the level and dispersion of organizational memory were moderate.

This study suggests that it is a very effective approach for service firms to initiate digital business strategy to improve service innovation performance. Furthermore, market intelligence responsiveness is crucial because it can help service firms quickly respond to market changes and adapt them accordingly. Managers of service firms should recognize that the benefits of digital business strategy are maximized only when the level and dispersion of organizational memory are moderate.

This study is the first to address the question of how and when digital business strategy drives service innovation performance in the context of digitization. In addition, this study enriches and advances organizational learning theory because it discusses the differential impact of digital business strategy on service innovation performance under varying degrees of organizational memory level and dispersion.

Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis.

Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO₂) and particulate matter 10 µm in diameter (PM₁₀). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models.

The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO₂ and PM₁₀ are expected to reach 10 and 127 µg/m³, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment.

This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.

This qualitative study aims to explore the experiences of Taiwan’s community-based long-term care service stations.

Semi-structured interviews were conducted with eight managers selected from stations located in the counties with the highest proportion of elderly people.

The results are as follows: the main services offered by the stations include health promotion activities, congregate meal programs, respite care and making house calls; government subsidies constitute a major proportion of the service stations’ funds, followed by user payments and external donations; the adversities encountered include frequent policy revisions, the dwellers’ reluctance to participate in the activities, manpower shortages and subpar service quality; and the effects of the stations on the community include achieving aging in place, providing more options for life after retirement, mitigating caregivers’ burden, expanding the elderly’s social networks and strengthening their health literacy.

The results of this research can understand the benefits and difficulties of Tier C service centers in Taiwan. Also, the practical experiences provide some suggestions for policies and training. Future studies can focus on establishing systematized training programs and standardizing the service personnel’s competence.

This paper aims to explore the impact of cycle superposition on bank liquidity risk under different levels of financial openness so that banks can better manage their liquidity risk. Meanwhile, it can also provide some ideas for banks in other emerging economies to better cope with the shocks of the global financial cycle.

Employing the monthly data of 16 commercial banks in China from 2005 to 2021 and based on the time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model, the authors first examine whether the cycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. Subsequently, the authors investigate the impact of different levels of financial openness on cycle superposition amplification. Finally, the shock of the financial cycle of the world's major economies on the liquidity risk of Chinese banks is also empirically analyzed.

Cycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. However, there are significant differences under different levels of financial openness. Compared with low financial openness, in the period of high financial openness, the magnifying effect of cycle superposition is strengthened in the short term but obviously weakened in the long run. In addition, the authors' findings also demonstrate that although the United States is the main shock country, the influence of other developed economies, such as Japan and Eurozone countries, cannot be ignored.

Firstly, the cycle superposition index is constructed. Secondly, the authors supplement the literature by providing evidence that the association between cycle superposition and bank liquidity risk also depends on financial openness. Finally, the dominant countries of the global financial cycle have been rejudged.

The purpose of this paper is to address the urgent need for transformation and upgrading in the construction industry amid the accelerating industrialization process. The focus is on understanding how value-added for construction enterprises can be achieved through servitization.

Grounded in the perspective of added value of construction enterprises, this paper uses a multicase analysis method, selecting four internationally renowned construction enterprises as the research subjects. The analysis investigates how these case study enterprises transition toward servitization and explores the trends in servitization in construction.

The research results indicate that servitization manifests in two aspects: contractor transition to servitization, and the shift of the construction industry toward integrated delivery. Furthermore, servitization in construction can achieve value-adding through three pathways: increasing product value, creating market demand and providing customized services.

This study contributes by providing insights into the manifestations of servitization and pathways for value-added. It provides a reference for the overall direction and basic strategy of servitization in construction.

Therapeutic landscapes can be beneficial for patients recovering from cognitive and behavioural impairments. The therapeutic garden in the Royal Talbot rehabilitation centre (RTRC) located in Kew, Melbourne is a precedent for its nature design outdoor spaces and horticultural therapy (HT) program for patients with cognitive impairments due to brain and spinal cord injuries. There have been no studies into the physical design of the RTRC therapeutic garden.

This case study unveils the critical design themes, and dominant physical elements of the RTRC therapeutic garden through site observation and analysis over the course of two months, a semi-structured interview with a key staff, and graphical representations using integrated photography and collaborative digital tools of AutoCAD and Adobe Photoshop.

Seven critical design themes are identified for the RTRC garden: (1) space layout and organization, (2) physical and visual access, (3) pathway and facility, (4) multisensory vegetation, (5) amenity, wayfinding and seasonal interest, (6) cognitive stimulation, (7) utility and HT. Qualitative and quantitative data analysed in visual and descriptive format reveal multisensory vegetation, amenity, wayfinding and seasonal interest, and utility and HT are the top three most pivotal critical design themes in the RTRC therapeutic garden.

The findings of this case study can be used to impart knowledge about the design of therapeutic landscape settings for cognitive impairments to design professionals and the public.

There is no case study has been conducted for this precedent of therapeutic garden in the Royal Talbot rehabilitation centre that targets for cognitive impairment patients. With the increasing awareness in medical and healthcare environments, this case study would help to inject mutual understanding, generate knowledge and design awareness among design professionals and public, and to cultivate more good quality healthcare settings.

This study aims to simultaneously examine customer complaints through the proposed novel Design for Six Sigma (DFSS) model which incorporates of creating the new insurance services to win customers' hearts and mind for the insurance industry.

A novel DFSS research methodology which includes the theory of inventive problem solving (TRIZ), Pugh concept selection, creative product analysis matrix and importance–satisfaction model (I–S Model) was proposed. In addition, a real insurance company case was studied to illustrate the effectiveness of the proposed DFSS model.

The results of a novel DFSS model not only can establish new services, but also can dramatically reduce the cost of resolving customer complaints.

The findings of this study are useful for insurance companies and other related service providers in devising tailored strategies to offer quality and suitable services to their customers.

This study addresses the paucity of research and marketing gaps through the proposed novel DFSS model for the first time in the insurance industry. These study findings would enable researchers and practitioners to formulate strategies for solving customer complaints effectively and develop new services from time to time.

This study adopts the elaboration likelihood model and configuration perspectives to explore the internal mechanisms underlying the influence of live streaming on consumer trust building and purchase intention.

This study invited 757 experienced live streaming e-commerce users from Chinese platforms such as TikTok and RED, who participated in survey by filling questionnaires collected online. The research employed a mixed-method approach using SEM and fsQCA. SEM was utilized to analyze quantitative data to determine the direct and mediated relationships within product trust, while fsQCA served as a complement to identify the combinations of conditions that enhance product trust.

The findings reveal three important insights. Firstly, in the context of live streaming e-commerce, both product characteristics and streamer characteristics significantly influence consumers' trust in products. The para-social interaction plays a partial mediating role in the relationship between streamer characteristics and product trust. Secondly, four distinct paths are identified that contribute to enhancing product trust in live streaming e-commerce. Thirdly, PSI emerging as a core condition across all four paths, underscores the importance for merchants to foster positive social interactions with consumers beyond the live streaming environment.

This study enhances understanding of the dynamic live streaming e-commerce industry, offering insights into consumer behavior and practical guidance for merchants seeking to build engaged, trustworthy customer relationships.

Knowledge adoption is the key to effective knowledge exchange in virtual question-and-answer (Q&A) communities. Although previous studies have examined the effects of knowledge content, knowledge source credibility and the personal characteristics of knowledge seekers on knowledge adoption in virtual Q&A communities from a static perspective, the impact of answer deviation on knowledge adoption has rarely been explored from a context-based perspective. The purpose of this study is to explore the impact of two-way deviation on knowledge adoption in virtual Q&A communities, with the aim of expanding the understanding of knowledge exchange and community management.

The same question and the same answerer often yield multiple answers. Knowledge seekers usually read multiple answers to make adoption decisions. The impact of deviations among answers on knowledge seekers' knowledge adoption is critical. From a context-based perspective, a research model of the impact of the deviation of horizontal and vertical answers on knowledge adoption is established based on the heuristic-systematic model (HSM) and empirically examined with 88,287 Q&A data points and answerer data collected from Zhihu. Additionally, the moderation effects of static factors such as answerer reputation and answer length are examined.

The negative binomial regression results show that the content and emotion deviation of horizontal answers negatively affect knowledge seekers' knowledge adoption. The content deviation of vertical answers is negatively associated with knowledge adoption, while the emotion deviation of vertical answers is positively related to knowledge adoption. Moreover, answerer reputation positively moderates the negative effect of the emotion deviation of horizontal answers on knowledge adoption. Answer length weakens the negative correlation between the content deviation of horizontal and vertical answers and knowledge adoption.

This study extends previous research on knowledge adoption from a static perspective to a context-based perspective. Moreover, information deviation is expanded from a one-way variable to a two-way variable. The combined effects of static and contextual factors on knowledge adoption are further uncovered. This study can not only help knowledge seekers identify the best answers but also help virtual Q&A community managers optimize community design and operation to reduce the cost of knowledge search and improve the efficiency of knowledge exchange.

The purpose of this study is to solve the problem that existing researches ignore the long-term and staged nature of digital transformation, failing to conduct specific discussions for different stages. It responded the call by constructing a three-stage evolutionary model to analyze the impact of digital transformation at different stages on the sustainable performance of manufacturing enterprises. The moderating effect of core technology capabilities is also explored, guided by the theory of assimilation innovation.

Based on the panel data of Chinese listed manufacturing companies from 2012 to 2020, this study empirically investigate the impact of digital transformation (digital process, digital operation and digital ecology) on sustainability performance (economic performance and environmental performance).

The findings indicate that digital operations and digital ecology significantly improve economic performance and environmental performance. Furthermore, the core technological capacity of the enterprise serves to modify the positive correlation between digital transformation at each stage and sustainable performance to some extent. In other words, when an enterprise is equipped with the requisite technological capacity, the digital transformation at each stage accelerates both economic performance and environmental performance, which in turn is conducive to an improvement in the enterprise’s sustainable development performance.

The findings contribute to the theoretical framework of digital transformation and sustainable development in all stages of enterprises. Furthermore, they provide guidance for achieving sustainable development through the implementation of digital transformation and the enhancement of core technological capacity.