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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.

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。

This paper aims to analyse the influence of workforce diversity on the firm’s likelihood to develop organisational innovations. Operationalising human resources diversity is not straightforward, and its effect has been rather overlooked in the context of non-technological innovations. This study analyses the impact of task-related diversity among research and development (R&D) unit workers and women R&D workers, in particular.

To estimate the impact of task-related diversity on firm propensity to undertake organisational innovation, this study uses a generalised linear model (GLM) – with a binomial family and log–log extension. GLMs are used to control problems of over-dispersion, which, in models with binary response variables, could generate inaccurate standard error estimates and provide inconsistent results.

This paper provides three important results. Firstly, employee diversity increases the firm’s propensity to engage in organisational innovations. Secondly, the influence of each facet of task-related diversity varies depending on the type of organisational innovation considered. Thirdly, gender has an effect on the innovation process; this study shows that women play a different role in the production of non-technological innovations.

This paper makes several contributions to the literature. Firstly, it makes a theoretical contribution to research on innovation management by considering the influence of human resources diversity on the development of non-technological innovations. Secondly, this study analyses the role of workforce diversity in an R&D department context to clarify the contribution made by women R&D workers.

This paper investigates the change talk (CT) strategies of the motivational interviewing (MI) technique and their relevance in achieving change goals within communities of practice (CoP), focusing on addressing real-world problems in today's complex world.

We employ a literature review and conceptual analysis to study the interactions and potential areas of complement between CT, MI and CoP theories.

This paper combines CT, MI and CoP theories to develop an integrated model called Facilitative Change Talk Leadership (FCTL).

This paper provides an innovative model (FCTL) to inform leadership educators about facilitating communities of practice. We provide a hypothetical case study to suggest how FCTL might foster collaborative inquiry and resilience amidst complex challenges. This case study illustrates a practical pathway for leadership educators and community practitioners to use this model in their own contexts.

The purpose of this paper is to fill the momentous gap by explicitly investigating the asymmetric effects of monetary policy (MP) on non-bank financial intermediation (NBFI) assets.

The authors utilized panel data from 29 countries for the period of 2012–2020 and used the quantile regression estimation. In addition to simultaneous quantile regression (SQR), the authors also employ quantile regression with clustered data (Parente and Silva, 2016) and the generalized quantile regression (GQR) method (Powell, 2020).

The empirical results show a significant heterogeneous impact of MP. While there is a positive relationship between MP and NBFI assets (“waterbed effect”) at lower quantiles of NBFI assets, at middle and higher quantiles, MP has a negative impact on NBFI assets (“search for yield” effect). The authors further find that negative impact strengthens as the quantile levels of NBFI assets rise from mid to high. Findings also reveal that “procyclicality” (except higher quantile) and “institutional demand” hypotheses hold. However, regarding “regulatory arbitrage,” mixed results are observed indicating the impact of Basel III requirements.

Previous empirical studies have concentrated on either the Dynamic Stochastic General Equilibrium (DSGE) framework or conditional mean regression approaches and delivered mixed findings of the MP effects on NBFI. The current paper takes a step toward dealing with this issue by deploying quantile regression methodology, which shows the impact of MP on NBFI at different conditional distributions (quantiles) of NBFI assets instead of just NBFI's conditional mean distribution.

This paper aims to document the impact of shadow banking on non-performing loans (NPLs) of publicly listed banks in an international setting.

This paper uses the data from 27 countries and various estimation strategies to test the arguments presented in this paper. The sample covers the period between 2002 and 2020.

The empirical results suggest that banks headquartered in countries with high shadow banking activity have fewer NPLs than otherwise similar banks headquartered in countries with low shadow banking activity. The findings remain qualitatively the same in different sub-samples and after replacing the main variables with their alternate proxies. The paper also shows that this relationship is sensitive to bank-specific characteristics. Moreover, the paper also indicates that the stringency of banking regulations weakens the relationship between shadow banking and NPLs.

The study’s data limitations prevent a detailed year-by-year analysis of NPLs and shadow banking, restricting insights into their evolving dynamics. In addition, the focus on country-level shadow banking data limits the exploration of how multinational banks’ activities in various jurisdictions impact individual banks’ NPLs.

The paper not only documents the effect of shadow banking on NPLs but also shows that the relationship between shadow banking and NPLs weakens as banking regulations become more stringent.

The purpose of this paper is to examine the evolution of interconnectedness of European insurers among themselves, as well as with other non-financial firms, for the period 2000–2021 and to analyze the stock return movements around the costliest catastrophic events (hurricanes) in the past two decades.

This paper follows the “simple” approach of Patro et al.(2013) and examines the daily stock return correlations of the largest 30 insurers and the largest 30 non-financial firms headquartered in Europe. In addition, the study uses event study methodology to examine stock return movements around the costliest hurricanes.

We find that the European insurance sector has become highly interconnected during the past two decades; however, its increasing connectedness with non-financial firms is limited to a few firms. In addition, we find weak evidence of the destabilizing effects of catastrophic events on European insurers and non-financial firms; however, the potential for cat risk contagion effects exists as the insurance industry becomes heavily interconnected.

The extant literature is largely concerned with the contribution of the insurance sector to the systemic risk of the financial sector. We focus on a specific region (Europe) and analyze the evolution of interconnectedness of the largest insurers within the insurance sector as well as with the largest non-financial firms encapsulating important crisis periods. In addition, we relate to the literature that examines the market reactions around catastrophic events to test the relevance of traditional insurance activities in instigating potential contagion shocks.

The primary purpose of this study is to explore the relationship between the unsolicited proposal (USP) and the performance of private participation infrastructure (PPI) projects in developing countries.

The main data set for this study was collected from the World Bank database consisting of 8,951 PPI projects that occurred in developing countries from 1996 to 2020. Hierarchical logistic regression was applied for investigating the effects of USPs on project success. Three moderators, namely, control of corruption, presence of local sponsor and project size were also included in the model to test the impact of their interactions with the USP on the performance of PPI projects. Further, to assess the impact of the effect of USPs, the average marginal effect was calculated. The framework used in this study consists of 18 control variables, three moderators and one noncontrolled independent variable (the USP).

The results of hierarchical logistic regression indicate that USPs have a significant and negative effect on the success of PPI projects occurring in developing countries. The negative effect of a USP weakens with the presence of local sponsors and stronger control of corruption in the host country. However, contrary to the authors’ expectations, the results show that project size does not significantly affect the association between USPs and the success of PPI projects. Moreover, the results of average marginal effects show that the negative impact of USP on the success of PPI projects ranges between 2.4% and 3.8%.

This study quantifies the negative impact of USP on the success of PPI projects in developing countries, which will be helpful for the practitioners to understand the associated risk with USP projects. Furthermore, it also identifies the moderating roles of control of corruption and the presence of local sponsors on the relationship between USP and the success of PPI projects.

The study aims to estimate the short- and long-run effects of monetary policy on residential property prices in South Africa. Over the past decades, there has been a monetary policy shift, from targeting money supply and exchange rate to inflation. The shifts have affected residential property market dynamics.

The Johansen cointegration approach was used to estimate the effects of changes in monetary policy proxies on residential property prices using quarterly data from 1980 to 2022.

Mortgage finance and economic growth have a significant positive long-run effect on residential property prices. The consumer price index, the inflation targeting framework, interest rates and exchange rates have a significant negative long-run effect on residential property prices. The Granger causality test has depicted that exchange rate significantly influences residential property prices in the short run, and interest rates, inflation targeting framework, gross domestic product, money supply consumer price index and exchange rate can quickly return to equilibrium when they are in disequilibrium.

There are limited arguments whether the inflation targeting monetary policy framework in South Africa has prevented residential property market boom and bust scenarios. The study has found that the implementation of inflation targeting framework has successfully reduced booms in residential property prices in South Africa.

This study aims to construct a mental health service system for middle school students in the post-COVID-19 era with the framework of Six Sigma DMAIC (define, measure, analyze, improve and control) and analyze the influencing factors of the mental health service system to study the implementation strategies of quality-oriented mental health services in middle schools.

This study was conducted in Tianjin, China, from September to November 2022, and 350 middle school students from Tianjin Public Middle School were selected as subjects. A questionnaire survey was used to collect data. In this study, the Six Sigma DMAIC method, sensitivity analysis method, exploratory factor analysis and principal component analysis were used to analyze the mental health services provided to middle school students.

Based on the Six Sigma DMAIC framework, this study indicates that the contribution rate of the mental health service process factor is the largest in the post-COVID-19 era. The mental health cultivation factor ranks second in terms of its contribution. Mental health quality and policy factors are also important in the construction of middle school students’ mental health service system. In addition, the study highlights the importance of parental involvement and social support in student mental health services during the post-COVID-19 era.

To the best of the authors’ knowledge, a study on middle school students’ mental health in the post-Covid-19 era has not yet been conducted. This study developed a quality-oriented mental health system and analyzed the influencing factors of mental health for middle school students based on data analysis and the Six Sigma DMAIC method.