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Understandings of house prices and their interrelationships have undoubtedly drawn a great amount of attention from various market participants. This study aims to investigate the monthly newly-built residential house price indices of seventy Chinese cities during a 10-year period spanning January 2011–December 2020 for understandings of issues related to their interdependence and synchronizations.

Analysis here is facilitated through network analysis together with topological and hierarchical characterizations of price comovements.

This study determines eight sectoral groups of cities whose house price indices are directly connected and the price synchronization within each group is higher than that at the national level, although each shows rather idiosyncratic patterns. Degrees of house price comovements are generally lower starting from 2018 at the national level and for the eight sectoral groups. Similarly, this study finds that the synchronization intensity associated with the house price index of each city generally switches to a lower level starting from early 2019.

Results here should be of use to policy design and analysis aiming at housing market evaluations and monitoring.

The qualitative approach was applied the discover the optimum answers to the research objectives, which are (1) to understand the cultural and hedonistic characteristics of the (Lanna) Songkran festival; and (2) to examine the dilemma between cultural rituals and hedonistic activity for tourism.

This study used a case study of the Songkran festival in Chiang Mai to examine the dilemma between cultural rituals and hedonism for tourism, which brings lost or misperceived cultural values and identities. The semi-structured interview (SSI) with senior locals and participant observation during the festival was conducted in Chiang Mai, Thailand, to obtain the in-depth phenomena of the existing celebration pattern at the festival.

The study findings show three crucial phenomena that explain characteristics of unsynchronized cultural rituals and hedonistic activities for tourism: (1) the parallel phenomenon between cultural values and celebration practice, (2) the movement of local culture and(3) the hedonistic characteristics of the festival.

The study extends the knowledge on the interplay phenomena between cultural festivals and tourism; also, the involved stakeholders, such as local communities, public sectors and private sectors, can use the study findings in creating policies for using cultural festivals to promote a destination and urban economic development that will minimise cultural values distort while increase tourism economic values.

This study was conducted qualitatively, including SSIs and participant observation at the Songkran festival in Chiang Mai. The study findings were analysed, based on the empirical data, into significant themes representing the characteristics of dilemma phenomena within the festival.

Allocation of the marketing budget has become increasingly challenging due to the diverse channel exposure to customers. This study aims to enhance global marketing knowledge by introducing an ensemble attribution model to optimize marketing budget allocation for online marketing channels. As empirical research, this study demonstrates the supremacy of the ensemble model over standalone models.

The transactional data set for car insurance from an Indian insurance aggregator is used in this empirical study. The data set contains information from more than three million platform visitors. A robust ensemble model is created by combining results from two probabilistic models, namely, the Markov chain model and the Shapley value. These results are compared and validated with heuristic models. Also, the performances of online marketing channels and attribution models are evaluated based on the devices used (i.e. desktop vs mobile).

Channel importance charts for desktop and mobile devices are analyzed to understand the top contributing online marketing channels. Customer relationship management-emailers and Google cost per click a paid advertising is identified as the top two marketing channels for desktop and mobile channels. The research reveals that ensemble model accuracy is better than the standalone model, that is, the Markov chain model and the Shapley value.

To the best of the authors’ knowledge, the current research is the first of its kind to introduce ensemble modeling for solving attribution problems in online marketing. A comparison with heuristic models using different devices (desktop and mobile) offers insights into the results with heuristic models.

COVID-19 has affected the economies adversely from all sides. The sudden halt in production has impacted both the supply and demand sides. It calls for analysis to quantify the impact of the reduction in economic activity on the economy-wide variables so that appropriate steps can be taken. This study aims to evaluate the sensitivity of various sectors of the Indian economy to this dual shock.

The eight-sector open economy general equilibrium Global Trade Analysis Project (GTAP) model has been simulated to evaluate the sector-specific effects of a fall in economic activity due to COVID-19. This model uses an economy-wide accounting framework to quantify the impact of a shock on the given equilibrium economy and report the post-simulation new equilibrium values.

The empirical results state that welfare for the Indian economy falls to the tune of 7.70% due to output shock. Because of demand–supply linkages, it also impacts the inter- and intra-industry flows, demand for factors of production and imports. There is a momentous fall in the demand for factor endowments from all sectors. Among those, the trade-hotel-transport and manufacturing sectors are in the first two positions from the top. The study recommends an immediate revival of the manufacturing and trade-hotel-transport sectors to get the Indian economy back on track.

The present study has modified the existing GTAP model accounting framework through unemployment and output closures to account for the impact of change in sectoral output due to COVID-19 on the level of employment and other macroeconomic variables.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

This case requires students to evaluate alternative dual-share-class corporate structures that allow companies and entrepreneurs to pursue profit with purpose. The case explores Impact Makers, an IT consulting company based in Richmond, Virginia. While original founders of the firm hold all voting rights, the cash flow rights belong to two nonprofits setting the stage for a Newman's Own model of management consulting. The case discusses whether and how the alternative corporate structure aids the firm's overall strategy to attract top-quality employees, pay them competitive salaries, and provide superior service to its clients while donating 100% of its lifetime value to charitable causes, largely through partnerships with various nonprofit organizations. More importantly, the case asks students to evaluate how such a dual-share-class and dual-purpose company can raise capital to fund continued growth.

The case opens with CEO Michael Pirron reminding himself of all the questions he had run through to execute a strategy to further grow Impact Makers' consulting business both through expanding a menu of services and through conquering new geographical markets. To do either, or both, the company needed a cash infusion. Internal cash was limited, as up to 40% of it flowed to charitable partners, demonstrating Impact Makers' commitment to its mission. Raising debt for a company without fixed assets was challenging and time consuming. Complicating it all was that being structured as a nonstock corporation rendered equity raising difficult. Could Impact Makers raise money to grow and stay true to community values at the same time?

This study aims to assess the effect of job insecurity, customer incivility and work–life imbalance on Lebanese bank employee workplace well-being (EWW), while investigating the moderating role that positive and negative affect might have.

Quantitative data was collected from 202 respondents and analyzed using structural equation modeling system through IBM SPSS and AMOS.

Results revealed that each of the independent variables has a negative, statistically significant effect on Lebanese bank EWW. The positive affect and the negative one are shown to have a moderating effect that lessens and boosts, respectively, these negative effects.

The study adds to the literature on EWW while highlighting the high-power distance and collectivist society that the research took place in.

Limitations include the sample size that was hoped to be larger, in addition to the self-reporting issue and what it entails in the data collection process.

The study has many practical implications, including the validation of a questionnaire in a developing Arab country, hence providing a reliable tool for researchers. HR specialists should lean toward applicants with positive affect, ensuring that their workplace is occupied by members with enhanced resilience. Furthermore, employers should support their employees’ professional growth, thus, boosting their employability during turmoil and consequently making them less vulnerable in times of economic recession.

The study’s unique context, depicted in the harsh economic and financial crisis, makes the findings on EWW of a high value.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.