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This study aims to investigate the impact of service quality (SQ), perceived value (PV) and consumer satisfaction on Thai street food, with customer satisfaction (CS) as a mediator for customer loyalty and repurchase intention (RI). It also explores how consumer trust (CT) in Thai street food safety moderates these relationships.

Structural equation modelling (SEM) was utilised to analyse the complex interrelationships between various constructs. Multi-group analyses were conducted to investigate the moderating effects of CT on the structural model, considering two distinct groups based on trust levels: low and high.

The findings revealed that SQ and PV significantly influenced CS and behavioural intention, while the perceived quality of Thai street food had no significant impact on post-COVID-19 consumer satisfaction. The study highlighted the critical role of CT in moderating the relationships between SQ, PV and CS, with distinct effects observed in groups with varying trust levels.

The research emphasises the importance of enhancing SQ and delivering value to customers in the context of Thai street food, which can contribute to increased CS, RI and positive word-of-mouth. Furthermore, the study underscores the critical role of building CT in fostering enduring customer relationships and promoting consumer satisfaction and loyalty.

This research offers valuable insights into consumer behaviour and decision-making processes, particularly within the realm of Thai street food. It underscores the significance of understanding and nurturing CT, especially in the post-COVID-19 landscape, emphasising the need for effective business strategies and consumer engagement.

This paper investigates how gender, ethnicity, and network membership interact to influence how small and medium-sized enterprise (SME) owner-managers become aware of finance support programmes developed by government policy and/or support schemes advanced by the banking industry.

Drawing on expectation states theory (EST), we develop eight sets of hypotheses and employ the UK SME Finance Monitor data to test them using bivariate probit regression analysis.

In general, network membership increases awareness, but more so for government programmes. We also find no differences between female and male owner-managers when in networks. However, we identify in-network and out-network differences by ethnicity, with minority females seemingly better off than minority males.

Business networks are better for disseminating government programmes than industry-led programmes. For native White women, network membership can enhance policy awareness advantage further, whilst for minorities, networks significantly offset the big policy awareness deficits minorities inherently face. However, policy and practice need to address intersectional inequalities that remain in access to networks themselves, information access within networks, and the significant out-network deficits in awareness of support programmes afflicting minorities.

This study provides one of the first large-scale empirical examinations of intersectional mechanisms in awareness of government and industry-led enterprise programmes. Our novel and nuanced findings advance our understanding of the ways in which gender and ethnicity interact with network dynamics in entrepreneurship.

Acknowledging, on the one hand, the increasing fragility of supply chains and the number of risks involved in supply chain operations and, on the other hand, the role of small- and medium-sized enterprises (SMEs) in supply chains and the high exposure of these firms to risks of different types, this study aims to examine the relationship between supply chain risk management (SCRM) and innovation performance in SMEs. Furthermore, the impact of technological turbulence on this relationship was studied to take into account recent technological changes.

Structural equation modelling was carried out on a sample of Turkish SMEs to test the hypotheses developed.

The findings presented allow the authors to better understand the link between SCRM and innovation performance in SMEs. More precisely, empirical evidence is provided about the impact of SCRM components such as maturity and ability on innovation performance. Furthermore, the findings show the impact of technological turbulence on both SCRM and innovation performance.

By focusing on SCRM in SMEs, this paper contributes to the body of knowledge with regard to SCRM in general and with regard to SMEs in particular; research on the latter has only started recently. Moreover, by having studied SMEs from a developing country (other than China), this paper helps to develop a broader and more diverse perspective of SCRM.

This paper analyses how firm births and deaths are influenced by previous firm births and deaths in related and unrelated sectors. Competition and multiplier effects are used as the theoretical lens for this analysis.

This paper uses 2008–2016 Irish business demography data pertaining to 568 NACE 4-digit sectors within 20 NACE 1-digit industries across 34 Irish county and sub-county regions within 8 NUTS3 regions. A three-stage least squares (3SLS) estimation is used to analyse the impact of past firm deaths (births) on future firm births (deaths). The effect of relatedness on firm interrelationships is explicitly modelled and captured.

Findings indicate that the multiplier effect operates mostly through related sectors, while the competition effect operates mostly through unrelated sectors.

This paper's findings show that firm interrelationships are significantly influenced by the degree of relatedness between firms. The raw data used to calculate firm birth and death rates in this analysis are count data. Each new firm is measured the same as another regardless of differing features like size. Some research has shown that smaller firms have a greater propensity to create entrepreneurs (Parker, 2009). Thus, it is possible that the death of differently sized firms may contribute differently to multiplier effects where births induce further births. Future research could seek to examine this.

These findings have implications for policy initiatives concerned with increasing entrepreneurship. Some express concerns that public investment into entrepreneurship can lead to “crowding out” effects (Cumming and Johan, 2019), meaning that public investment into entrepreneurship could displace or reduce private investment into entrepreneurship (Audretsch and Fiedler, 2023; Zikou et al., 2017). This study’s findings indicate that using public investment to increase firm births could increase future firm births in related and unrelated sectors. However, more negative “crowding out” effects may also occur in unrelated sectors, meaning that public investment which stimulates firm births in a certain sector could induce firm deaths and crowd out entrepreneurship in unrelated sectors.

This paper is the first in the literature to explicitly account for the role of relatedness in firm interrelationships.

The current paper is part of a larger scoping review project investigating the intersection of leader(ship) identity development and meaning-making. In this review, we analyzed 100 articles to determine the current extent of literature that covers the intersection of leader(ship) identity development, meaning-making and marginalized social identities.

A review of the extant literature is included, and a conceptual model is suggested for further exploration into this critical and under-researched domain.

More research is needed at the intersection of leadership identity development, meaning-making and marginalized social identities.

As this area of study has expanded, scholars have noted an absence of research on the effect of multiple social identities, especially marginalized identities, on meaning-making and leadership identity construction.

Driven by Davis’s (1989) technology acceptance model (TAM) and Westaby’s (2005) behavioral reasoning theory (BRT), the purpose of this study is to develop and test a conceptual model and examine consumers’ acceptance of artificial intelligence (AI) chatbots for apparel shopping.

Data from 353 eligible US respondents was collected through a self-administered questionnaire distributed on Amazon Mechanical Turk, an online panel. Confirmatory factor analysis and path analysis were used to test all hypothesized relationships using the structural equation model.

The results show that optimism and relative advantage of “reasons for” dimensions have a positive and significant influence on perceived ease of use (PEU), while innovativeness and relative advantage have a positive and significant influence on perceived usefulness (PUF). Discomfort and insecurity have no significant impact on PEU and PUF. However, complexity has a negative and significant impact on PEU but not on PUF. Additionally, PEU has a positive influence on PUF. Both PEU and PUF have a positive and significant influence on consumers’ attitudes toward using AI chatbots, which, in turn, affects the intention to use AI chatbots for apparel shopping. Overall, this study identifies that optimism, innovativeness and relative advantage are enablers and good reasons to adopt AI chatbots. Complexity is a prohibitor, making it the only reason against adopting AI chatbots for apparel shopping.

This study contributes to the literature by integrating TAM and BRT to develop a research model to understand what “reasons for” and “reasons against” factors are enablers or prohibitors that significantly impact consumers’ attitude and intention to use AI chatbots for apparel shopping through PEU and PUF.

This study illustrates the economic and financial dynamics of the sector, analysing the evolution of the main ratios of profitability and financial structure of 1,559 Italian real estate companies divided into the three macro-regions: North, Centre and South, in the period 2011–2020. In this way, it is also possible to verify the responsiveness to the 2020 pandemic crisis.

The analysis uses descriptive statistics tools and the ANOVA method of analysis of variance, supplemented by the Tukey–Kramer test, to identify significant differences between the three Italian macro-regions.

The study shows the increase in profitability after the 2008 crisis, despite its reverberation in the years 2012–2013. The financial structure of companies improved almost everywhere. The pandemic had modest effects on performance.

In the future, other indices should be considered to gain a more comprehensive view. This is a quantitative study based on financial statements data that neglects other important economic and social factors.

Public policies could use this study for better interventions to support the sector. In addition, internal management can compare their company's performance with the industry average to identify possible improvements.

The research analyses an economic field that employs a large number of people, especially when considering the construction and real estate services covered by this analysis.

The study contributes to the literature by providing a quantitative analysis of industry dynamics, with comparative information that can be deduced from financial statements over the years.

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。

The business ambiguity because of COVID-19 has brought the tourism industry under stress. Using the service-dominant-logic and elaboration-likelihood-model, this study tested the effects of destination-based cognitive, affective and behavioral customer brand engagement (CBE) on customer brand co-creation (CBC). This research also examined the effects of involvement and CBC on customer revisit intention (CRI) during the COVID-19 pandemic. This study also tested the moderating role of customers’ age among the modeled relationships.

Investigating these matters, a sample of 315 tourists was recruited and adopted a mixed-method approach, including structural equation modeling (SEM) as well as fuzzy set qualitative-comparative analysis (fsQCA).

SEM results render that CBE’s dimensions exercise different impacts on CBC, which affect revisit-intention. Results ascertain customer involvement’s direct effects on CBC and revisit intention. Multi-group analysis uncovers that consumer age significantly moderates the CBC and CRI relationship, and their effect increases as consumers get older. The fsQCA results revealed more heterogenous combinations to predict CBC and revisit intention.

This study focuses on CBE, CBC and involvement, and contributes unique insight to tourism marketing research; thus, it identifies plentiful opportunities for further research, as summarized.

This study offers key implications for destinations to build tourism/marketing strategies to strengthen the CBE/CBC or tourist/destination–brand relationship.

Though CBE/CBC and involvement are identified as important research priorities, empirically derived insights among these and related factors remain limited in the course of the COVID-19 crisis.

本文采用结构方程模型(SEM)和模糊集定性比较分析(fsQCA)相结合的方法, 对315名游客进行了调查。

由于新型冠状病毒感染症(COVID-19)产生的业务不定性给旅游业带来了压力。本研究运用服务主导逻辑和精细似然模型, 检验了基于目的地的认知、情感和行为顾客品牌参与(CBE)对顾客品牌共同创造(CBC)的影响。本研究还考察了COVID-19大流行期间参与和CBC对客户重访意愿(CRI)的影响。检验了顾客年龄在模型关系中的调节作用。

SEM结果表明, CBE的维度对CBC有不同的影响, 而这种影响又会影响着重游意愿。结果确定了游客参与对CBC和重访意愿的直接影响。多群体分析发现, 消费者年龄显著调节CBC和CRI关系, 且随着消费者年龄的增长, 其作用增强。fsQCA结果显示需更多的异质组合来预测CBC和再访意向。

-本研究关注CBE、CBC和参与, 为旅游营销研究提供了独特的见解, 因此总结出了许多进一步研究的机会。

本研究为目的地建立旅游/营销策略以加强CBE/CBC或游客/目的地-品牌关系提供了重要启示。

尽管CBE/CBC和参与被认为重要的研究重点, 但在covid −19危机期间, 从这些因素和相关因素中得出的经验见解仍然有限。

Para investigar estas cuestiones, se seleccionó una muestra de 315 turistas y se utilizó un enfoque metodológico mixto que incluía el modelo de ecuaciones estructurales (SEM) y el análisis cualitativo-comparativo de conjuntos difusos (fsQCA).

La confusión empresarial debida a la pandemia del COVID-19 ha sometido al sector turístico a una fuerte tensión. Utilizando la lógica dominante del servicio y el modelo de elaboración de verosimilitud, este estudio examinó los efectos del compromiso cognitivo, afectivo y comportamental del cliente con la marca del destino (CBE) en la cocreación de la marca (CBC). Esta investigación también analizó los efectos de la implicación y la CBC en la intención de revisita (IRC) durante la pandemia COVID-19. Este estudio también evaluó el papel moderador de la edad de los clientes entre las relaciones establecidas.

Los resultados del SEM muestran que las dimensiones de la CBE ejercen diferentes impactos sobre la CBC, que afectan a la intención de revisita. Los resultados determinan los efectos directos de la implicación del cliente sobre la CBC y la intención de revisita. El análisis multigrupo revela que la edad del consumidor modera significativamente la relación entre el CBC y el IRC, y que su efecto aumenta a medida que los consumidores envejecen. Los resultados del fsQCA revelaron combinaciones más heterogéneas para predecir el CBC y la intención de volver a visitar.

Este estudio se centra en la CBE, la CBC y la implicación, y aporta una visión única a la investigación del marketing turístico, por lo que identifica numerosas oportunidades para futuras investigaciones.

Este estudio ofrece implicaciones clave para que los destinos construyan estrategias de turismo/marketing en el fortalecimiento de la relación CBE/CBC o turista/destino-marca.

Aunque la CBE/CBC y la implicación se identifican como importantes prioridades de investigación, las percepciones derivadas empíricamente entre estos factores y otros relacionados siguen siendo limitadas en el transcurso de la crisis del COVID-19.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.