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This study aims to explore the connection between demographic traits and the choice of attraction patterns among international repeat tourists.

The study employed a questionnaire survey to collect data from 1550 international repeat tourists who visited Tanzania between November 2022 and July 2023. Convenient sampling was employed as tourists were selected from the three international airports of Tanzania, namely Kilimanjaro International Airport, Julius Nyerere International Airport, and Abeid Aman Karume International Airport. A multinomial logistic regression model was used to examine the impact of socio-demographic characteristics on the selection of attraction patterns among international repeat tourists.

The study revealed that demographic factors, including age, marital status, income level, occupation, and education level, exhibit statistically significant correlations with preferences for distinct attraction patterns. This significance was established through a p-value of less than 0.05 for all the aforementioned variables.

This study is primarily focused on international repeat tourists, thereby limiting insights into the preferences of domestic tourists. To better inform strategies aimed at attracting a larger domestic tourist base, future research may prioritize the investigation of choice of attractions patterns among domestic tourists in relation to their demographic characteristics.

This study contributes to the nuanced understanding of international tourist behavior by unraveling the extent to which demographic traits impact tourists’ choices of attraction patterns, thereby providing insights crucial for effective marketing strategies, improved visitor experiences, and sustainable tourism development strategies.

This article describes financial professionals' perceptions of their clients' financial behaviors and the explanatory factors underlying these behaviors.

In this qualitative research, the authors seek to understand financial professionals' experiences in relation to how their clients manage their own finances. The authors conduct and analyze 26 semi-structured interviews with financial professionals from several industries within the financial sector in Canada.

The professionals in this study noted that despite their clients' financial knowledge, several other factors can explain these individuals' financial behaviors. They include psychological factors (such as financial bias, the need for instant gratification, and the lack of awareness regarding the long-term effects of certain types of financial behaviors), financial habits (such as lifestyle, financial planning and lack of discipline) and the financial system's flexibility with respect to debt financing and repayment. These perceptions are categorized according to whether they are related to debt financing or repayment, savings or investments.

By using a qualitative methodology that relies on the perceptions of financial professionals, this study aims to better understand the financial behaviors of individuals and households, and these behaviors' underlying factors. This study's findings could be useful to various stakeholders interested, in one way or another, in financial literacy, such as organizations aiming to strengthen and promote financial literacy, educators, researchers, regulatory bodies of financial institutions and financial advisers.

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。

Sports marketing and sponsorship research is located at the intersection of behavioral and design research, which means that it analyzes the current world and shapes a future world. This dual focus poses challenges for formulating and testing theories of sports marketing.

This article develops criteria for categorizing theoretical concepts as either behavioral or formed as different ways of expressing ideas of sports marketing research. It emphasizes the need for clear concept categorization for proper operationalization and applies these criteria to selected theoretical concepts of sports marketing and sponsorship research.

The study defines three criteria to categorize theoretical concepts, namely (1) the guiding idea of research, (2) the role of observed variables, and (3) the relationship among observed variables. Applying these criteria to concepts of sports marketing research manifests the relevance of categorizing theoretical concepts as either behavioral or formed to operationalize concepts correctly.

This study is the first in sports marketing to clearly categorize theoretical concepts as either behavioral or formed, and to formulate guidelines on how to differentiate behavioral concepts from formed concepts.

This study examines experiences and enjoyment of national parks in the context of Tanzania.

A cross-sectional design with quantitative and qualitative methods is applied. The study area is Nyerere National Park in Tanzania. Data collected from fully completed structured questionnaires by 360 domestic tourists are subjected to descriptive statistics and Partial Least Square Structural Equation Modeling analysis. Content analysis is used to analyze qualitative data.

The findings have revealed that there is a significant relationship between direct experiences and enjoyment of southern national parks among domestic tourists.

The suggestion is for future studies to explore a longitudinal approach to determine the patterns of domestic tourists in reference to experiences and enjoyment of national parks so as to improve domestic tourism.

The practical implication is for the government, private sector and tourism stakeholders to improve infrastructure and conduct regular surveys and tour guide training.

This study examines experiences and enjoyment of national parks in the context of Tanzania and, specifically analyzes the relationship between direct experiences and enjoyment of southern national parks among domestic tourists in Tanzania guided by types of tourists’ theory.

The purpose of this paper is to investigate and quantify the impact of product complexity, including architectural complexity, on operator learning, productivity and quality performance in both assembly and disassembly operations. This topic has not been extensively investigated in previous research.

An extensive experimental campaign involving 84 operators was conducted to repeatedly assemble and disassemble six different products of varying complexity to construct productivity and quality learning curves. Data from the experiment were analysed using statistical methods.

The human learning factor of productivity increases superlinearly with the increasing architectural complexity of products, i.e. from centralised to distributed architectures, both in assembly and disassembly, regardless of the level of overall product complexity. On the other hand, the human learning factor of quality performance decreases superlinearly as the architectural complexity of products increases. The intrinsic characteristics of product architecture are the reasons for this difference in learning factor.

The results of the study suggest that considering product complexity, particularly architectural complexity, in the design and planning of manufacturing processes can optimise operator learning, productivity and quality performance, and inform decisions about improving manufacturing operations.

While previous research has focussed on the effects of complexity on process time and defect generation, this study is amongst the first to investigate and quantify the effects of product complexity, including architectural complexity, on operator learning using an extensive experimental campaign.

Experiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance. Uncertainty inherently affects experiment measurements and is often missing in the available data sets due to its estimation cost. For similar reasons, experiments are very few compared to other data sources. Discarding experiments based on the missing uncertainty values would preclude the development of predictive models. Data profiling techniques are fundamental to assess data quality, but some data quality dimensions are challenging to evaluate without knowing the uncertainty. In this context, this paper aims to predict the missing uncertainty of the experiments.

This work presents a methodology to forecast the experiments’ missing uncertainty, given a data set and its ontological description. The approach is based on knowledge graph embeddings and leverages the task of link prediction over a knowledge graph representation of the experiments database. The validity of the methodology is first tested in multiple conditions using synthetic data and then applied to a large data set of experiments in the chemical kinetic domain as a case study.

The analysis results of different test case scenarios suggest that knowledge graph embedding can be used to predict the missing uncertainty of the experiments when there is a hidden relationship between the experiment metadata and the uncertainty values. The link prediction task is also resilient to random noise in the relationship. The knowledge graph embedding outperforms the baseline results if the uncertainty depends upon multiple metadata.

The employment of knowledge graph embedding to predict the missing experimental uncertainty is a novel alternative to the current and more costly techniques in the literature. Such contribution permits a better data quality profiling of scientific repositories and improves the development process of data-driven models based on scientific experiments.

Service managers increasingly strive to achieve sustainability through strategies centered on circularity. With a focus on saving, extending and (re)generating resources and their enclosing service systems, circularity can contribute to environmental, social and financial gains. Yet, the notion of circularity is surprisingly understudied in service research. This article seeks to provide an initial conceptual understanding of circular service management, introducing illustrative strategies and research priorities for circular service management. This paper provides a roadmap for scholars, practitioners and policymakers to develop a deeper understanding of the opportunities from adopting circular services.

The authors explore the concept of circular service management by drawing upon existing literature on sustainability, circularity and service research. Strategies of circular service management and research priorities emerge on the basis of industry best practice examples and research on sustainability challenges and opportunities.

Service researchers have largely ignored the concept and role of circularity for service businesses. Extant research on the topic nearly exclusively features in non-service journals and/or does not seek to advance service theory through circularity. This article argues that circular service management enables the implementation of service thinking in the pursuit of sustainability and outlines four types of circular service management strategies.

The authors introduce the concept of circular service management and highlight the role of service research for designing and managing circular systems and operations. This article also offers a research agenda connecting managerial challenges and opportunities with key service research priorities for circular service management. This provides a roadmap for scholars, practitioners and policymakers to develop a deeper understanding of pursuing circular services, thereby contributing to a more sustainable future.

This study aims to propose a tailored Lean Six Sigma framework providing an accessible Lean Six Sigma methodology for compound feed manufacturers with the aim of mitigating rising costs and increasingly complex demands from customers.

A Lean Six Sigma framework was designed combining Lean value stream mapping and Six Sigma structured problem-solving with a case study in an Irish compound feed manufacturer.

The study found that the Lean Six Sigma implementation framework provided a simplified approach, which fitted the resource availability within compound feed manufacturing.

The study is limited by the constraints of a sole case study in providing empirical evidence of the effectiveness of the framework. Nevertheless, a conceptual Lean Six Sigma model is proposed, which will assist compound feed manufacturers implementing a continuous improvement approach.

This paper proposes a simplified approach to the implementation of Lean Six Sigma in agricultural compound feed manufacturers and in small and medium-sized organisations. This is the first such study in Ireland and will add to the body of work on Lean in agriculture and aid other agri-businesses and compound feed manufacturers in understanding how Lean Six Sigma can benefit.

The primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.

The authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.

This paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.

The authors identify several gaps in the literature which this research does not address but could be the focus of future research.

The authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.

Bankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.

To the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.