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Pre-construction of a project comprises stages that are pivotal for the procurement performance. It is defined as the duration from the project's initiation to construction. However, Private Public Partnerships (PPPs) have been subjected to a long pre-construction, thereby leading to an inefficient development process. Therefore, the purpose of this paper is to pay attention to the influencing factors elongating the pre-construction duration.

Based on data of 5,677 PPP projects between 2009 and 2021 in China, the authors adopt the Accelerated Failure Time (AFT) model in duration analysis to empirically analyze the following underlying dynamics determining the duration of PPP pre-construction stages: (1) policy uncertainty; (2) corruption; and (3) procurement method selection. To observe the influencing paths more specifically, the authors divided the pre-construction duration into the pre-tendering period and tendering period and regressed them separately.

The results indicate that the pre-construction duration is significantly prolonged with increased policy uncertainty and corruption degree as well as the use of tendering methods. Meanwhile, the above factors have a greater impact on the pre-tendering period than the tendering period.

The contribution of this study is twofold: (1) theoretically, this paper provides new evidence on the impact of PPP policy uncertainty, corruption and procurement method selection on the pre-construction duration. It complements empirical studies on the factors elongating the time efficiency of PPPs projects. (2) In practice, it provides a specific path for the government to improve the time efficiency of PPPs.

Completing Right-of-Way (ROW) acquisition process on schedule is critical to avoid delays and cost overruns on transportation projects. However, transportation agencies face challenges in accurately forecasting ROW acquisition timelines in the early stage of projects due to complex nature of acquisition process and limited design information. There is a need of improving accuracy of estimating ROW acquisition duration during the early phase of project development and quantitatively identifying risk factors affecting the duration.

The quantitative research methodology used to develop the forecasting model includes an ensemble algorithm based on decision tree and adaptive boosting techniques. A dataset of Georgia Department of Transportation projects held from 2010 to 2019 is utilized to demonstrate building the forecasting model. Furthermore, sensitivity analysis is performed to identify critical drivers of ROW acquisition durations.

The forecasting model developed in this research achieves a high accuracy to predict ROW durations by explaining 74% of the variance in ROW acquisition durations using project features, which is outperforming single regression tree, multiple linear regression and support vector machine. Moreover, number of parcels, average cost estimation per parcel, length of projects, number of condemnations, number of relocations and type of work are found to be influential factors as drivers of ROW acquisition duration.

This research contributes to the state of knowledge in estimating ROW acquisition timeline through (1) developing a novel machine learning model to accurately estimate ROW acquisition timelines, and (2) identifying drivers (i.e. risk factors) of ROW acquisition durations. The findings of this research will provide transportation agencies with insights on how to improve practices in scheduling ROW acquisition process.

The purpose of this study is to examine the differential impact of qualitative and quantitative informational signals within the merger and acquisition (M&A) press releases on deal completion and duration. A significant percentage of deals by emerging market acquirers get abandoned before completion, and those that are completed have a longer duration. The limited information about the operations of acquirers from emerging markets creates suspicion among the stakeholders involved in deal resolution, hindering the completion of deals. Thus, using the signal-feedback paradigm, authors investigate how informational signals in the M&A press release impact the deal resolution.

The study employs content analysis on M&A press releases announced by firms from five emerging economies: Brazil, Russia, India, China and South Africa. The technique is applied based on the exploration-exploitation framework developed by March (1991) to categorize the announced deal motives (qualitative information). Next, the authors identify the percentage of relevant quantitative information disclosed in the press release, following which results are obtained using logistic and ordinary least square regressions.

The study reports that deals with declared exploratory motives take longer to complete. Additionally, deals disclosing higher percentage of quantitative disclosure exhibit lower completion rate and increased deal duration.

This is the first study to provide evidence that familiarity bias impacts deal duration as relative to exploitation deals that are familiar to the stakeholders; exploratory deals take longer to conclude. Further, our analysis indicates that a greater percentage of quantitative disclosure may not always reduce information risk but rather be interpreted negatively in the form of the acquirer’s overconfidence in the deal’s potential.

The study utilises unique survey data gathered from 12,427 party members. The dependent variable measures party members’ in-party commitment and is based on willingness to donate money, to contribute effort, the feeling of belonging in the party network and social trust in the party network.

In this article, we study how different extra-parliamentary online and offline activities are associated with in-party commitment amongst political party members from the six largest Finnish parties. We especially delve into the differences between members of the Finnish parties.

We found that extra-parliamentary political activity, including connective action through social media networks and collective action through civic organisations, is highly associated with members’ in-party commitment. Additionally, members of the newer identity parties more effectively utilised social media networks, whilst the traditional interest parties were still more linked to traditional forms of extra-parliamentary political action.

By employing the sociological network theory perspective, the study contributes to ongoing discussions surrounding the impact of social media on political participation amongst party members, both within and beyond the confines of political parties.

This paper aims to examine the causation linking financial technology to economic growth in the East African Community states from 1997 to 2019.

Autoregressive distributed lag is used. Gross domestic product per capita proxies economic growth, automated teller machines, point of sale, debit card ownership and mobile banking measure financial technology.

The results unveil a significant relationship between financial technology and economic growth. The findings show bidirectional causality between automated teller machine and economic growth, with unidirectional causation from economic growth to point of sales and internet banking, mobile banking and government effectiveness to economic growth. The error correction term is negatively significant, demonstrating a long-term convergence between Fintech measures and economic growth.

The governments should effectively enact and implement policies that protect investments in financial technologies to boost economic growth in the East African Community countries. The government should reduce taxes on financial technology equipment and related services. The use of automated teller machine, debit card ownership and internet banking should be encouraged through cashless transactions. Financial institutions should adopt cashless operation policies to encourage the use of financial technologies.

Research results on the bond between financial technology and economic growth are not conclusive. These studies demonstrate that technological innovations are double edged-swords, with both positive and negative sides. The results are conflicting; some reveal positive relationships, while others show negative links. Hence, research is required to fill the lacuna.

This study aims to assess how real income, oil prices and gold prices affect housing prices in China from 2010 to 2021.

This study uses a novel bootstrap autoregressive distributed lag (ARDL) testing to empirically analyze the short and long links among the tested variables.

The ARDL estimations demonstrate a positive impact of oil price shocks and real income on housing market prices in both the phrases of the short and long run. Furthermore, the results reveal that gold price shocks negatively affect housing prices both in the short and long run. The result can be attributed to China’s housing market and advanced infrastructure, resulting in a drop in housing prices as gold prices increase. Additionally, the prediction of housing market prices will provide a base and direction for housing market investors to forecast housing prices and avoid losses.

To the best of the authors’ knowledge, this is the first attempt to analyze the effect of gold price shocks on housing market prices in China.

Purpose: Students’ transition from education to employment is influenced by factors like the length and calibre of their education, demography, labour market conditions, and the general state of the economy. Regardless of the economy, education systems should seek to ensure that students have the skills required for the labour market. This will help them better transition from school to work. This study examines the work skills that companies require for entry-level positions in Romania.

Need for Study: Previously, text analysis studies treated the job market only for the IT industry in Romania. To understand the demand-side opportunities and restrictions, assessing the employment opportunities for young people in the Romanian labour market is necessary.

Methodology: A text mining approach from 842 unstructured data of the existing job positions in October 2022 for fresh graduates or students is used in this chapter. The study uses data from LinkedIn job descriptions in the Romanian job market. The methodology involved is focused on text retrieval, text-pre-processing, word cloud analysis, network analysis, and topic modelling.

Findings: The empirical findings revealed that the most common words in job descriptions are experience, team, work, skills, development, knowledge, support, data, business, and software. The correlation network revealed that the most correlated pairs of words are gender–sexual–race–religion–origin–diversity–age–identity–orientation–colour–equal–marital.

Practical Implications: This study looked at the job market and used text analytics to extract a space of skill and qualification dimensions from job announcements relevant to the Romanian employment market instead of depending on subjective knowledge.

The case study is based on a social entrepreneurial journey where the authors have used an interview method to get the insights from the protagonists and the employees. Rigorous interviews were conducted online and in person for deep analysis of the protagonist’s strategies and decisive dilemma.

Secondary data was collected from company’s website for facts and figures.

This case study is a story of indigenous tribes of Odisha from the eyes of a woman who, with her co-founder, empathized with their vulnerable life and took on the challenge of creating sustainable livelihoods by establishing Millet Magic Foundation. The Millet Magic Foundation was established in 2021 by Shyama and her cofounder to uplift the indigenous tribe of Mayurbhanj by providing them livelihood through millet-based products. The foundation launched their millet-based snack products with the brand name WOWMOM. Millet Magic Foundation created social impact for the tribals by providing them with employment, fair wages, health care and social well-being. The specialty of the Millet Magic was reverse positioning and focusing on the bottom of the pyramid. The success of the Millet Magic Foundation relied on its mission to uplift the life of these indigenous tribal, especially the women, by overcoming the challenges with the strategies to establish Millet Magic as a social enterprise.

The case study is primarily suitable for postgraduate programme to teach the concept of social entrepreneurship in the entrepreneurship module. The case study can also be used for highlighting the role of social enterprise in sustainable economic development of emerging economies.

Prior studies on the impact of incentives on physicians’ online participation mainly focused on different incentives while ignoring the difficulty of setting monetary incentives efficiently. Based on goal-setting theory, the current research examines the relationship between incentives with goals of varying difficulty and professional health knowledge sharing (PHKS) in online health knowledge-sharing platforms (OHKSPs).

Four field experiments with different monetary incentives were conducted by one of China’s largest OHKSPs, with whom the researchers cooperated in data collection. Monthly panel data on 10,584 physicians were collected from September 2018 to December 2019. There were 9,376 physicians in the treatment group and 1,208 in the control group. The authors used a difference-in-difference (DID) model to explore the research question based on the same control group and the Chow test with seemingly unrelated estimation (sureg) to compare regression coefficients between four groups. Several robustness checks were performed to validate the main results, including a relative time model, multiple falsification tests and a DID estimation using the propensity score matching method.

The results show that the monetary incentive significantly positively affected the volume of physicians’ PHKS directly with negative spillover to the duration of physicians’ PHKS. Moreover, the positive effect of incentives with higher difficulty on the volume of physicians’ PHKS was significantly smaller than that of incentives with low difficulty. Finally, professional title had a positive moderating effect on the volume of goal difficulty setting and did not significantly moderate the effect on the duration of physicians’ PHKS.

Some limitations of this study are: firstly, because the field experiments were enterprise benefit oriented, the treatment and control groups were not balanced. Secondly, the experiments for different incentive measures were relatively similar, making it challenging to validate a causal effect. Finally, more consideration should be given to the strategy for setting hierarchical incentives in future research.

The research indicates that monetary incentives have a bilateral effect on PHKS, i.e. a positive direct effect on the volume of physicians’ contributions and a negative spillover effect on the duration of physicians’ PHKS. The professional titles of physicians also moderate such bilateral switches of PHKS. Furthermore, when a physician’s energy is limited, the goal difficulty setting of the incentive mechanism tends to be low. The more difficult the incentives are, the more inefficient the effects on physicians’ PHKS will be.

The study aims to examine the indirect relationships via application (app) brand self-relevance emotions and self-relevance that underlie the relationships between perceived value of mobile apps and (brand) love with respect to mobile apps. The study further investigates the moderating role of user–app relationship duration in the formation process of brand love for mobile apps from a dynamic and long-term perspective.

A multiple moderated-mediation model is developed and empirically tested with a sample of 396 users of popular Chinese mobile educational apps.

The study reveals that utilitarian value exhibits positive indirect relationships with brand love for mobile apps through increased positive self-relevance emotions. All three types of perceived value of mobile apps (utilitarian, hedonic and social) affect app brand love positively via self-relevance. These three types of perceived value were found to be serially linked to brand love through self-relevance and self-relevance emotions. Furthermore, empirical evidence is found for the moderating effects of user–app relationship duration.

By testing mechanisms simultaneously in an integrative model, this study investigates the reasons for app brand love that attract a user into a lasting relationship with an app and extends knowledge of the app brand love building process in inducing strong and positive brand–self connections. Our study also makes practical contributions by offering insights into delivering the most desired benefits to mobile app users according to different contextual conditions, in order to attract and retain users in a more cost-effective manner.