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The purpose of this paper is to accomplish a bibliometric analysis, investigate the underlying knowledge structure, founding and development, and evolution of the Journal of Knowledge Management (JKM) through its articles published between 1997 and 2020.

A total of 1,346 research papers from JKM were selected and VantagePoint^® software was used to generate bubble maps, auto-correlation maps, and matrix maps through techniques such as principal component analysis (PCA) and natural language processing (NLP). The analysis gives insights about the foundation of knowledge structure, its evolution and the development of JKM.

The systematic mapping of research illustrates topics emerging as new offshoots, global favourites, saturated and plateaued and reached academic maturity. The USA, the UK, Australia, Spain, Italy, China, Canada, Germany, and France have contributed the most to JKM. This paper provides a robust roadmap for future research investigation of JKM.

The authors humbly admit the possibility of overlooking some research papers while evaluating and filtering the database of JKM. The research outcome summarizes 23 years, subject to information retrieval from archival files.

This research is a detailed bibliometric analysis explaining paradigm shifts in the body of knowledge of JKM. The bibliometric outcomes can act as beacons for future researchers and academicians to revisit the current trends that shape the domain of knowledge management, particularly for the JKM audience with a focus on contemporary topics of research interest.

This is a unique endeavour to accomplish a systematic bibliometric analysis of the JKM for two decades, offering insights about its structural body of knowledge through an overview of the chronology of scholarly development in the field of knowledge management.

Travel time at inter-stops is a set of important parameters in bus timetabling, which is usually assumed to be normal (log-normal) random variable in literature. With the development of digital technology and big data analytics ability in the bus industry, practitioners prefer to generate deterministic travel time based on the on-board GPS data under maximum probability rule and mean value rule, which simplifies the optimization procedure, but performs poorly in the timetabling practice due to the loss of uncertain nature on travel time. The purpose of this study is to propose a GPS-data-driven bus timetabling approach with consideration of the spatial-temporal characteristic of travel time.

The authors illustrate that the real-life on-board GPS data does not support the hypothesis of normal (log-normal) distribution on travel time at inter-stops, thereby formulating the travel time as a scenario-based spatial-temporal matrix, where K-means clustering approach is utilized to identify the scenarios of spatial-temporal travel time from daily observation data. A scenario-based robust timetabling model is finally proposed to maximize the expected profit of the bus carrier. The authors introduce a set of binary variables to transform the robust model into an integer linear programming model, and speed up the solving process by solution space compression, such that the optimal timetable can be well solved by CPLEX.

Case studies based on the Beijing bus line 628 are given to demonstrate the efficiency of the proposed methodology. The results illustrate that: (1) the scenario-based robust model could increase the expected profits by 15.8% compared with the maximum probability model; (2) the scenario-based robust model could increase the expected profit by 30.74% compared with the mean value model; (3) the solution space compression approach could effectively shorten the computing time by 97%.

This study proposes a scenario-based robust bus timetabling approach driven by GPS data, which significantly improves the practicality and optimality of timetable, and proves the importance of big data analytics in improving public transport operations management.

Physical gatherings at social events have been found as one of the main causes of COVID-19 transmission all over the world. Smartphone has been used for contact tracing by exchanging messages through Bluetooth signals. However, recent confirmed cases found in venues indicated that indirect transmission of the causative virus occurred, resulting from virus contamination of common objects, virus aerosolization in a confined space or spread from inadequate ventilation environment with no indication of human direct or close contact observed.

This paper presents a novel cyber-physical architecture for spatial temporal analytics (iGather for short). Locations with time windows are modeled as digital chromosomes in cyberspace to represent human activity instances in the physical world.

Results show that the high spatial temporal correlated but indirect tracing can be realized through the deployment of physical hardware and spatial temporal analytics including mobility and traceability analytics. iGather is tested and verified in different spatial temporal correlated cases. From a management perspective of mobilizing social capacity, the venue plays not only a promotion role in boosting the utilization rates but also a supervision-assisted role for keeping the venue in a safe and healthy situation.

This research is of particular significance when physical distancing measures are being relaxed with situations gradually become contained. iGather is able to help the general public to ease open questions: Is a venue safe enough? Is there anyone at a gathering at risk? What should one do when someone gets infected without raising privacy issues?

This study contributes to the existing literature by cyber-physical spatial temporal analytics to trace COVID-19 indirect contacts through digital chromosome, a representation of digital twin technology. Also, the authors have proposed a venue-oriented management perspective to resolve privacy-preserving and unitization rate concerns.

This study aims to examine the evolution of spatial–temporal patterns in China’s hotel industry from 1978 to 2018.

A database comprising over 140,000 hotels with more than 30 rooms was created. The exploratory spatial–temporal data analysis (ESTDA) method, based on space–time cube model, was used to explore and visualize the spatial–temporal pattern of hotels.

The Chinese hotel industry can be divided into two development stages, namely, a large hotel-dominant stage before 2000 and a small–medium-sized hotel-dominant stage after 2000. China’s prefecture-level cities were clustered into four tiers. The higher the tier, the earlier the city will initiate hotel development. The Chinese hotel industry has four continuous hotspots (the Yangtze River Delta, Pearl River Delta, Bohai Rim and Sichuan and Chongqing) and some temporary hotspots.

This study lacks quantitative investigation, which could show the underlying mechanism of the evolution of the Chinese hotel industry.

This study is the first to investigate China’s hotel evolution over 40 years by applying big data and the ESTDA method. The systematic and evolutionary exploration will enable hotel researchers to understand the spatial–temporal nature of hotel distribution better. Introducing the ESTDA method into tourism and hotel research also provides an additional tool to researchers. Hotel investors and operators, city and tourism planners and market regulators can learn from the evolution of location patterns to make better where and when decisions.

Amidst the plethora of mass communication methods that technology bestowed business with, print advertisements still remain an effective and widely utilized advertising tool, and retain a diachronically venerable position in international marketing practice. Bar and transcending mere academic fascination or curiosity, this research provides insights into the past, an understanding of the present and an outlook into the future. In this vein, through a methodical and comprehensive critical review of extant literature on print advertisements since 1965, this research aims to identify gaps in extant knowledge, to map its trends and divergences, to trace its paradigm shifts and to ultimately develop agendas for truly significant future research.

This spatial-temporal study reviews 256 methodically selected articles, using VantagePoint software, and adopts a novel methodology through natural language processing (NLP), text mining, auto-correlation maps, and bubble maps to conduct and present a robust analysis and explicit findings.

Using also the VOSviewer for density and network visualization, the results identify the predominant literature themes and, conversely, the relatively under-researched areas, and provide a more insightful collective interpretation of extant works, while laying the foundation for future research of greater value and significance to academia and industry.

This study transcends the partial and/or limited analyses and perspectives of extant literature to present scholars with the first comprehensive and long term meta-analysis or systematic study of print advertising, with explicit findings of both scholarly and executive worth.

The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.

Online learning is well-known by its flexibility of learning anytime and anywhere. However, how behavioral patterns tied to learning anytime and anywhere influence learning outcomes are still unknown.

This study proposed concepts of time and location entropy to depict students’ spatial-temporal patterns. A total of 5,221 students with 1,797,677 logs, including 485 on-the-job students and 4,736 full-time students, were analyzed to depict their spatial-temporal learning patterns, including the relationships between identified patterns and students’ learning performance.

Analysis results indicate on-the-job students took more advantage of anytime, anywhere than full-time students. Students with a higher tendency for learning anytime and a lower level of learning anywhere were more likely to have better outcomes. Gender did not show consistent findings on students’ spatial-temporal patterns, but partial findings could be supported by evidence in neural science or by cultural and geographical differences.

A more accurate approach for categorizing position and location might be considered. Some findings need more studies for further validation. Finally, future research can consider connections between other well-known performance predictors (such as financial situation, motivation, personality and major) and the type of learning patterns.

The findings gained from this study can help improve the understandings of students’ learning behavioral patterns and design as well as implement better online education programs.

This study proposed concepts of time and location entropy to identify successful spatial-temporal patterns of on-the-job and full-time students.

This paper aims to propose a new theoretical perspective on the organizational design of offshoring service organizations by adopting an information processing perspective which incorporates the factors of collaborative information technologies, task commoditization and global customer service delivery that are characteristic of modern-day knowledge-intensive service (KIS) organizations.

The authors analyze data from a large multiyear survey of offshoring service providers conducted in 12 countries.

The authors show how use of collaborative technology is significantly and positively related to spatial and configurational dispersion, task commoditization is significantly and positively related to spatial and temporal dispersion and need for global customer presence is not related to spatial, temporal or configurational dispersion.

The paper integrates concepts from management information system (MIS), operations management and international business to show how collaborative technology, task characteristics and customer service requirements affect the global dispersion of KISs.

The results show how use of collaborative technology, task characteristics and global customer service requirements need to be jointly considered in the global dispersion of activities by KIS providers.

The study sheds light on the effect of the key factors on different dimensions of global dispersion (i.e. spatial/temporal/configurational dispersion) in offshoring service provider organizations. Second, it shows how the traditional information processing perspective on organizations can be updated and applied to KIS organizations by incorporating the factors of global collaborative information technologies, task commoditization and global customer service.

While the economic benefits of the exhibition industry for the hotel sector have been addressed, the impact of exhibitions on individual hotels is unknown, especially when individual hotels’ star classification and locations are considered. This study aims to provide a better understanding of how room rates of different hotels change during different stages of the Canton Fair in China from a spatial-temporal perspective.

Room rates of 681 star-hotels within the city of Guangzhou before, during and after the Fair were extracted from websites. Through spatial interpolation and autocorrelation analysis and geographical detector (GeoDetector) technique, spatial and temporal patterns of hotel room rates and the interdependence between the convention center and the hotels with different star classification and locations were examined.

An inverse-U shape of room rate change was identified before, during and after the Fair, and the five-star hotels had the sharpest increase. Moreover, the distribution of hotel room rates followed the law of distance decay. The variation of hotel rates became larger when the distance to the convention center was larger. Spatial high-high clusters varied among hotels with different star classification.

This study contributed to the hotel literature by providing empirical evidence regarding how hotels with different star classification and locations were affected by events. This study also advanced the event literature by introducing GeoDetector. The findings of this study offered insights into the hotel location selection, pricing strategies and hotel collaboration with events.

虽然展览业对酒店业的经济效益已经得到解决, 但展览对单个酒店的影响尚不清楚, 尤其是在考虑单个酒店的星级和位置时。本研究旨在从时空角度更好地了解中国广交会不同阶段不同酒店的房价变化情况。

网站提取了广交会前、中、后广州市内681家星级酒店的房价。通过空间插值和自相关分析以及地理探测器（GeoDetector）技术, 研究了酒店房价的时空格局以及会议中心与不同星级和位置的酒店之间的相互依赖关系。

会前、会中、会后房价变化呈倒U型, 其中五星级酒店涨幅最大。此外, 酒店房价的分布遵循距离衰减规律。到会展中心的距离越远, 酒店价格的变化就越大。不同星级酒店的空间高-高集群存在差异。

该研究通过提供关于不同星级和位置的酒店如何受到事件影响的经验证据, 为酒店文献做出了贡献。这项研究还通过引入 GeoDetector 推进了事件文献。研究结果为酒店选址、定价策略和酒店与活动的合作提供了见解。

On 24 January 2020, France informed WHO of three cases of novel coronavirus, all of whom had travelled from Wuhan, China. These three cases were the first confirmed cases in Europe. By 13 March 2020, Europe had become the epicentre of the pandemic with more reported cases and deaths than the rest of the world combined, apart from the People’s Republic of China. Many European countries like Italy, France and Germany took drastic actions and subsequently announced a lockdown, while other countries like the UK, the Netherlands and Sweden were much more hesitant to introduce such far-reaching safety measures. This chapter provides a literature overview of how the variation in such measures in Europe has ultimately resulted in changes in daily activities and travel behaviour during the pandemic. It focusses on five main themes: (i) reduction in mobility and activities, (ii) spatial-temporal adjustments in out-of-home activities in which people still participated, (iii) modal adjustments especially among people who used to travel by public transport before the pandemic, (iv) new out-of-home activities including new outdoor activities and (v) digital adaptations as several out-of-home activities were replaced by digital activities, with special attention to the experience of teleworking.