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This paper examines the relationship between transport connectivity and regional economic development in China. It develops measurements appropriate for transport connectivity based on a set of evaluation models. This model is used to analyze the logistic connectivity of China’s 31 provinces by focusing on 11 variables, including some new factors (Density of road network, Density of railway network, Number of Internet Users) not used in previous studies, over the 13-year period from 2002 to 2014. Using panel data regression analysis, the empirical results show a statistically significant and positive impact of transport connectivity (factors like Density of road network, Density of railway network and Number of Internet Users) on economic development in China. In particular, the Number of internet users is a key factor reflecting information connectivity in all the variables. Comparative analysis regarding economic development is conducted to benchmark between coastal provinces and interior provinces. Like most previous research, this study yields the same finding of higher impact of transport connectivity on economic development in eastern provinces than in western provinces. This study suggests that decentralized decision-making will be significantly more efficient for analyzing regional infrastructure development. It also shows that the influence of transport connectivity on economic development is dependent on a certain developmental stage. This suggests that an economic region should adopt different development strategies for transport connectivity during different stages of development.

The purpose of this paper is to identify the impact of traditionally unmonitored energy sources and sinks on assessment of the as-built thermal performance of occupied homes. The analysis aims to demonstrate the potential scale of uncertainties introduced in a heat balance estimation of the heat transfer coefficient (HTC) when using in-use monitored data.

Energy flows for two UK homes – one a 1930s dwelling with high heat loss, the second a higher-performing 2014-built home – are predicted using the UK Government’s standard assessment procedure (SAP) and visualised using Sankey diagrams. Selected modelled energy flows are used as inputs in a quasi-steady state heat balance to calculate in-use HTCs as if from measured data sets gathered in occupied homes. The estimated in-use HTCs are compared against SAP-calculated values to illustrate the impact of including or omitting various heat sources and sinks.

The results demonstrate that for dwellings with low heat loss, the increased proportion of heating demand met by unmetered internal and solar gains informs a greater sensitivity of a heat balance estimation of the HTC to their omission. While simple quasi-steady state heat balance methods may be appropriate for dwellings with very high heat loss, alternative approaches are likely to be required for those with lower heat loss.

A need to understand the impacts of unmetered heat flows on the accuracy with which a building’s thermal performance may be inferred from in-use monitored data is identified: this paper illustrates the scale of these impacts for two homes at opposite ends of the energy performance scale.

The purpose of this paper is to examine peer-to-peer sharing platform business models, their sources of competitive advantage, and the roles, motivations and behaviors of key actors in their ecosystems.

This paper uses a conceptual approach that is rooted in the service, tourism and hospitality, and strategy literature.

First, this paper defines key types of platform business models in the sharing economy anddescribes their characteristics. In particular, the authors propose the differentiation between sharing platforms of capacity-constrained vs capacity-unconstrained assets and advance five core properties of the former. Second, the authors contrast platform business models with their pipeline business model counterparts to understand the fundamental differences between them. One important conclusion is that platforms cater to vastly more heterogeneous assets and consumer needs and, therefore, require liquidity and analytics for high-quality matching. Third, the authors examine the competitive position of platforms and conclude that their widely taken “winner takes it all” assumption is not valid. Primary network effects are less important once a critical level of liquidity has been reached and may even turn negative if increased listings raise friction in the form of search costs. Once a critical level of liquidity has been reached, a platform’s competitive position depends on stakeholder trust and service provider and user loyalty. Fourth, the authors integrate and synthesize the literature on key platform stakeholders of platform businesses (i.e. users, service providers, and regulators) and their roles and motivations. Finally, directions for further research are advanced.

This paper helps platform owners, service providers and users understand better the implications of sharing platform business models and how to position themselves in such ecosystems.

This paper integrates the extant literature on sharing platforms, takes a novel approach in delineating their key properties and dimensions, and provides insights into the evolving and dynamic forms of sharing platforms including converging business models.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

This paper aims to investigate the effect of voluntary non-financial reporting on the evaluation of audit risk from the auditors’ viewpoint in a post-crisis period. Furthermore, this paper analyses whether auditors perceive that voluntary non-financial reporting impacts audit risk differently for old clients as compared with new clients.

This study is conducted on a sample of Italian audit firms through a paper-based questionnaire. Both Big4 and non-Big4 audit firms have been included in the sample.

Results show that integrated reporting is perceived to be the most relevant reporting method and intellectual capital statement the least relevant. Surprisingly, empirical findings over the sample period show that auditors do not perceive statistically significant differences between old and new clients.

Auditors can identify opportunities to adapt their assessment model to include voluntary non-financial report information. Moreover, they can use different assessment models regarding the research variables in the case of new and old clients.

Empirical findings highlight the growing role of voluntary non-financial reporting in the auditors’ perception of their client’s audit risk. All the observed voluntary non-financial reporting forms, except for intellectual capital, are considered as relevant by auditors in the evaluation of their client’s audit risk when compared to an indifference point. In addition, findings reveal that female auditors perceive a reduced gap in the relevance between integrated reports and intellectual capital reports compared to their counterparts.

This paper aims to investigate how actors in the farmer’s network influence the adoption of smart farming technology (SFT) and to understand how social media affects this adoption process, in particular focusing on the influence of social media on trust in knowledge dissemination within the network.

The methodology used a two-stage process, with semi-structured interviews of farmers, augmented by a netnographic approach appropriate to the social media context.

The analysis illustrates the key role of the farmer network in the dissemination of SFT knowledge, bringing insight into an important B2B context. While social media emerges as a valuable way to connect farmers and promote discussion, it remains underused in knowledge dissemination on SFT. Also, farmers exhibit more trust in the content from peers online rather than from SFT vendors.

Novel insights are gained into the influence of the farming network on the accelerated adoption of SFT, including the potential role of social media in mitigating the homophilous nature of peer-to-peer interactions among farmers through exposure to more diverse actors and information. The use of a social network theory lens has provided new insights into the role of trust in shaping social media influence on the farmer, with variances in farmer trust of information from technology vendors and from peers.

Accurate values for infiltration rate are important to reliably estimate heat losses from buildings. Infiltration rate is rarely measured directly, and instead is usually estimated using algorithms or data from fan pressurisation tests. However, there is growing evidence that the commonly used methods for estimating infiltration rate are inaccurate in UK dwellings. Furthermore, most prior research was conducted during the winter season or relies on single measurements in each dwelling. Infiltration rates also affect the likelihood and severity of summertime overheating. The purpose of this work is to measure infiltration rates in summer, to compare this to different infiltration estimation methods, and to quantify the differences.

Fifteen whole house tracer gas tests were undertaken in the same test house during spring and summer to measure the whole building infiltration rate. Eleven infiltration estimation methods were used to predict infiltration rate, and these were compared to the measured values. Most, but not all, infiltration estimation methods relied on data from fan pressurisation (blower door) tests. A further four tracer gas tests were also done with trickle vents open to allow for comment on indoor air quality, but not compared to infiltration estimation methods.

The eleven estimation methods predicted infiltration rates between 64 and 208% higher than measured. The ASHRAE Enhanced derived infiltration rate (0.41 ach) was closest to the measured value of 0.25 ach, but still significantly different. The infiltration rate predicted by the “divide-by-20” rule of thumb, which is commonly used in the UK, was second furthest from the measured value at 0.73 ach. Indoor air quality is likely to be unsatisfactory in summer when windows are closed, even if trickle vents are open.

The findings have implications for those using dynamic thermal modelling to predict summertime overheating who, in the absence of a directly measured value for infiltration rate (i.e. by tracer gas), currently commonly use infiltration estimation methods such as the “divide-by-20” rule. Therefore, infiltration may be overestimated resulting in overheating risk and indoor air quality being incorrectly predicted.

Direct measurement of air infiltration rate is rare, especially multiple tests in a single home. Past measurements have invariably focused on the winter heating season. This work is original in that the tracer gas technique used to measure infiltration rate many times in a single dwelling during the summer. This work is also original in that it quantifies both the infiltration rate and its variability, and compares these to values produced by eleven infiltration estimation methods.

Logistical facility location decisions can make a crucial difference in the success or failure of a company. Geographical Information Systems (GIS) have recently become a very popular decision support system to help deal with facility location problems. However, until recently, GIS methodologies have not been fully embraced as a way to deal with new facility location problems in business logistics. This research makes a framework for categorizing empirical facility location problems based on the intensity of the involvement of GIS methodologies in decision making. This framework was built by analyzing facility location models and GIS methodologies. The research results revealed the depth of the embracement of GIS methodologies in logistics for determining new facility location decisions. In the new facility location decisions, spatial data inputs are almost always coupled with the visualization of the problems and solutions. However, the usage of GIS capability solely (i.e. suitability analysis) for problem solving has not been embraced at the same level. In most cases, the suitability analysis is used together with special optimization models for choosing among the multiple alternatives.

The paper debunks Peter P. Li’s assertion that Yin-Yang is superior to any other cognitive frames or logical systems for paradox research. The purpose of this paper is to alert the Chinese indigenous management researchers to the danger of Chinese exceptionalism and over-confidence.

To show that Peter P. Li’s assertion is doubtful, the authors identify the flaws in his analysis.

The authors find that there are three serious flaws in Peter P. Li’s analysis. First, there are four defects in the typology of cognitive frames he built in order to compare Yin-Yang with the others. Second, his understanding of dialectics in general and Hegelian dialectics in particular is flawed. And finally, without resorting to Yin-Yang, many scholars can develop theories that are equivalent to those derived from Yin-Yang.

Due to the page limit, this paper only focuses on arguing that Yin-Yang is not superior to other cognitive frames or logical systems without going one step further to explain in which situations Yin-Yang are valuable and might be more suitable than others for helping us understand some research issues.

This paper implies that we should not blindly believe that the Chinese way of thinking and acting is superior to other people’s. Chinese people should be open-minded in the globalized era, not only promoting their own culture but also appreciating and learning from other cultures.

The reduction of cultural exceptionalism and ethnocentrism can make cross-cultural communication and interaction smoother.

This paper is a rigorous critique on the “Yin-Yang being superior” assertion of Peter P. Li.

The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

This study is a narrative review of the literature.

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.