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This study investigates the effectiveness of microfinance-backed entrepreneurship as a mechanism for the holistic empowerment of women.

This study employs a mixed-method research-design consisting of quasi-experimental design (quantitative approach) involving women, both entrepreneurs (132) and non-entrepreneurs (238), as well as in-depth semi-structured interviews (qualitative approach).

Quantitative analysis revealed that female entrepreneurs are better off than female non-entrepreneurs in terms of economic, social, political and psychological indicators of empowerment. However, relatively lesser impact was found in terms of political, and to an even smaller extent, social empowerment of women. Analysis of in-depth interviews corroborated these findings confirming that entrepreneurship serves as an effective tool for the holistic empowerment of women. However, non-entrepreneurs also exhibit social empowerment.

Given the restricted geographical ambit of the study, prudence ought to be exercised in drawing inferences applied to alternate contexts. That the vast majority of questionnaire respondents are illiterate presented a notable impediment in the process of collection of accurate responses.

Microfinance intervention ought to be specifically directed to cultivating entrepreneurship among women; in particular, to achieve the full benefits of empowerment, women availing microfinance ought to exert full control over their own business ventures.

In analyzing holistic empowerment through microfinance supported businesses set up by women, the study adds to the existing literature on women entrepreneurship and empowerment.

In recent years, the robotic process automation (RPA) technology has increasingly been used to automate business processes. While a lot of research has been published on the potential and benefits of the technology, only a few studies have conducted research on challenges related to RPA adoption. Hence, this study aims to identify and discuss challenges related to RPA implementation projects.

Following an inductive methodology, interviews have been conducted with consultants who were involved in multiple RPA implementation projects. Hence, their extensive experience and views contribute to a detailed and in-depth understanding of the phenomena under research.

The results suggest that there are various process-related, technical, resource-related, psychological and coordinative challenges that must be considered when conducting an RPA implementation project.

This paper contributes to knowledge by presenting a new typology of challenges, as well as providing an in-depth discussion of the individual challenges that organizations face.

Incineration has become increasingly important in many large cities around the world because of fast urbanization and population growth. The benefits of energy production and large reduction in the waste volume to landfills also contribute to its growing adaptation for solid waste management for these cities. At the same time, the environmental impact of the pollutant gases emitted from the incineration process is a common concern for various stakeholders which must be properly addressed. To minimize the pollutant gas emission levels, as well as maximize the energy efficiency, it is critically important to optimize the combustion performance of an incinerator freeboard which would require the development of reliable approaches based on computational fluid dynamics (CFD) modeling. A critical task in the CFD modeling of an incinerator furnace requires the specification of waste characteristics along the moving grate as boundary conditions, which is not available in standard CFD packages at present. This study aims to address this gap by developing a numerical incinerator waste bed model.

A one-dimensional Lagrangian model for the incineration waste bed has been developed, which can be coupled to the furnace CFD model. The changes in bed mass due to drying, pyrolysis, devolatilization and char oxidation are all included in the model. The mass and concentration of gases produced in these processes through reactions are also predicted. The one-dimensional unsteady energy equations of solid and gas phases, which account for the furnace radiation, conduction, convection and heat of reactions, are solved by the control volume method.

The Lagrangian model is validated by comparing its prediction with the experimental data in the literature. The predicted waste bed height reduction, temperature profile and gas concentration are in reasonable agreement with the observations.

The simplicity and efficiency of the model makes it ideally suitable to be used for coupling with the computational furnace model to be developed in future (so as to optimize incinerator designs).

In this paper the interconnectedness among financial institutions and the level of systemic risks of four types of Chinese financial institutions are investigated.

By the means of RAS algorithm, the interconnection among financial institutions are illustrated. Different methods, including Linear Granger, Systemic impact index (SII), vulnerability index (VI), CoVaR, and MES are used to measure the systemic risk exposures across different institutions.

The results illustrate that big banks are more interconnected and hold the biggest scales of inter-bank transactions in the financial network. The institutions which have larger size tend to have more connection with others. Insurance and security companies contribute more to the systemic risk where as other institutions, such as trusts, financial companies, etc. may bring about severe loss and endanger the financial system as a whole.

Since other institutions with low levels of regulation may bring about higher extreme loss and suffer the whole system, it deserves more attention by regulators considering the contagion of potential risks in the financial system.

This study builds a valuable contribution by examine the systemic risks from the perspectives of both interconnection and tail risk measures. Furthermore; Four types financial institutions are investigated in this paper.

The purpose of this paper is to exploit a database of pre-existing designs to accelerate parametric optimization of antenna structures is investigated.

The usefulness of pre-existing designs for rapid design of antennas is investigated. The proposed approach exploits the database existing antenna base designs to determine a good starting point for structure optimization and its response sensitivities. The considered method is suitable for handling computationally expensive models, which are evaluated using full-wave electromagnetic (EM) simulations. Numerical case studies are provided demonstrating the feasibility of the framework for the design of real-world structures.

The use of pre-existing designs enables rapid identification of a good starting point for antenna optimization and speeds-up estimation of the structure response sensitivities. The base designs can be arranged into subsets (simplexes) in the objective space and used to represent the target vector, i.e. the starting point for structure design. The base closest base point w.r.t. the initial design can be used to initialize Jacobian for local optimization. Moreover, local optimization costs can be reduced through the use of Broyden formula for Jacobian updates in consecutive iterations.

The study investigates the possibility of reusing pre-existing designs for the acceleration of antenna optimization. The proposed technique enables the identification of a good starting point and reduces the number of expensive EM simulations required to obtain the final design.

The proposed design framework proved to be useful for the identification of good initial design and rapid optimization of modern antennas. Identification of the starting point for the design of such structures is extremely challenging when using conventional methods involving parametric studies or repetitive local optimizations. The presented methodology proved to be a useful design and geometry scaling tool when previously obtained designs are available for the same antenna structure.

Buildings and their use is a complex process from design to occupation. Buildings produce huge volumes of data such as building information modelling (BIM), sensor (e.g. from building management systems), occupant and building maintenance data. These data can be spread across multiple disconnected systems in numerous formats, making their combined analysis difficult. The purpose of this paper is to bring these sources of data together, to provide a more complete account of a building and, consequently, a more comprehensive basis for understanding and managing its performance.

Building data from a sample of newly constructed housing units were analysed, several properties were identified for the study and sensors deployed. A sensor agnostic platform for visualising real-time building performance data was developed.

Data sources from both sensor data and qualitative questionnaire were analysed and a matrix of elements affecting building performance in areas such as energy use, comfort use, integration with technology was presented. In addition, a prototype sensor visualisation platform was designed to connect in-use performance data to BIM.

This work presents initial findings from a post occupancy evaluation utilising sensor data. The work attempts to address the issues of BIM in-use scenarios for housing sector. A prototype was developed which can be fully developed and replicated to wider housing projects. The findings can better address how indoor thermal comfort parameters can be used to improve housing stock and even address elements such as machine learning for better buildings.

The purpose of this paper is to explore differences in the degree of innovation in employment relations (ER) between emerging and established firms,

A large national telephone survey (N=1,416) of both emerging (<5 years) and established firms was conducted.

Emerging firms were more casualised, less unionised, and experiencing higher levels of market expansion and unpredictability. Despite these differences, younger firms showed otherwise remarkable similarity to older firms across a range of ER practices, and both categories showed a reliance on business networks, rather formal training, for ER knowledge. While introducing ER changes more rapidly than older (and larger) firms, they were converging towards a suite of ER practices similar to that adopted by older firms. The results suggest that, if anything, established firms may have been engaged in greater innovation in more unusual ER practices.

Only managers were surveyed. The data are cross-sectional rather than longitudinal. As the study was undertaken in only one country, replication in other settings would be desirable.

The results raise major doubts about the notion that new firms represent the cutting edge of innovation, and highlights the degree to which newer firms match or mimic older firms’ ER architecture.

This paper aims to investigate the impact of uncertainty on the predictive power of term spread and its components for future stock market returns and economic activity in Korea and the USA. This paper finds that the stock market’s expected excess return and growth of economic activity are positively related to the risk-neutral expectation, one of the term spread’s components, particularly during high uncertainty periods. These findings are consistent with the importance of the monetary policy by the central bank in a high uncertainty environment created by unexpected shocks. The results are robust to alternate definitions of high uncertainty periods.

Radical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination.

This study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables.

The results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism.

This study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.

2019冠狀病毒病引致消費者習慣有根本的改變; 這些改變顯示，根據歷史序列而運作的慣常需求預測技巧未必是正確的。這不確性尤以受到大流行極大影響的酒店服務需求為甚。因此，我們擬探討、若把在推特網站上的旅遊活動視為聖雅各之路 (一個重要的朝聖旅遊聖地) 酒店服務需求的預測器，這會否是合適的呢?

本研究比較 SARIMA 時間序列模型與附有外生變數 (SARIMAX)模型兩者在預測旅遊及酒店服務需求方面的表現。為此，研究人員收集在推特網站上發佈的資訊，作為外生變數進行研究。這個樣本涵蓋於2018年1月至2022年9月期間110,456個發佈資訊。

研究結果確認了傳統的時間序列模型，若涵蓋推特網站上的旅遊活動，則其對旅遊需求方面的預測會得到顯著的改善。推特網站的數據，就改善預測實時旅遊需求的準確度，或許可成為有效的工具; 而這發現對旅遊管理會有一定的意義。本研究亦讓我們進一步瞭解朝聖旅遊方面旅客的數碼足跡。

現存文獻甚少探討朝聖旅遊的數字化，而本研究不但在這方面充實了有關的文獻，還使用了一個根據推特網站上使用者原創內容嶄新的方法框架，進行分析和探討。這會幫助酒店從業人員把社交媒體數據轉變為可供酒店管理之用的合宜資訊。

Shadow banks or non-bank financial intermediaries (NBFIs) are facilitators of credit, especially in emerging market economies (EMEs). However, there are certain risks associated with them, such as their unchecked leverage and interconnectedness with the rest of the financial system. In light of this, the present study analyses the impact of the growth of shadow banks on the stability of the banking sector and the overall stability of the financial system. The authors further examine the effect of the growth of finance companies (a type of NBFIs) on financial stability.

The study employs data of 11 EMEs (monitored by the Financial Stability Board (FSB)) for the period 2002–2020 to examine the above relationships. Panel-corrected standard errors method and Driscoll–Kray standard error estimation are deployed to conduct the analysis.

The results signify that the growth of the shadow banking sector and the growth of lending to the shadow banking sector are negatively associated with the stability of the banking sector and increases the vulnerability of the financial system (overall instability). This implies that the higher the growth of the shadow banks, the higher the financial fragility. Finance companies are also found to negatively affect financial stability. These findings are validated by different estimation methods and point out the risks posed by the NBFI sector.

The extant study builds a composite index (Financial Vulnerability Index (FVI)) to measure financial stability; thus, the findings contribute to the evolving literature on shadow banks.