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A global crisis like that caused by the COVID-19 pandemic threatens the survival of any business, but especially of nascent entrepreneurs, due to their vulnerable situation. At this stage of entrepreneurship, information and communication technology capabilities (ICTCs) are critical skills that help entrepreneurs develop their new businesses, fostering economic adaptability to counteract adverse effects. This study advances knowledge of how nascent entrepreneurs react in an environment of global crisis.

The study analyzes a sample of 331 Spanish nascent entrepreneurs to determine the mediating effect of ICTCs on the relationship between the impact of a global crisis (e.g. COVID-19) and the firm’s strategic response.

The results suggest that crises influence adaptation and compensation strategies significantly and that ICTCs exert a total mediating effect on this relationship. The results do not, however, establish a clear relationship between the impact of the COVID-19 crisis and disengagement response, but rather a negative relationship, possibly influenced by government attempts to mitigate the pandemic’s economic consequences (economic aid to maintain the workforce, financial support for business model survival).

The COVID-19 crisis revealed ICT as a key technology for continuing business operations. This study analyzes how ICTCs affect nascent entrepreneurs’ strategies in crisis environments. Our analysis is important because these entrepreneurs have invested resources in their new project. We must determine their strategic response to crisis environments: adaptation, compensation or disengagement. The sample itself, collected during the pandemic, provides unique insights into the impact of the crisis on nascent business decisions.

像2019冠狀病毒病大流行等的全球危機一旦發生，各工商企業能否繼續生存必會受到威脅和影響。這影響以剛開始發展的創業者為甚，因為他們處於脆弱的處境。在這個創業階段，創業者必須擁有資訊與通訊科技能力，才能發展他們的新業務，他們亦需培養經濟上的適應能力，以能抵銷各種不利的影響。本研究擬就剛開始發展的創業者在全球危機發生時應如何應對進行探討，以增進我們對這課題的知識。

本研究分析一個涵蓋331名西班牙新生創業者的樣本，來鑒定資訊與通訊科技能力對全球危機 (如2019冠狀病毒病) 帶來的影響與企業戰略應對之間的關聯所起的中介效應。

研究結果似顯示，危機會顯著地影響企業的適應和賠償策略; 研究結果似乎也顯示，資訊與通訊科技能力會對這關聯 (全球危機所帶來的影響與企業戰略應對之間的關聯) 發揮極大的中介效應。但研究結果並沒有就2019冠狀病毒病危機的影響與脫離反應、建立明確的關聯。反之，研究結果似顯示兩者有一個負相關的關係，這可能是因為政府施行應對方法，以減輕大流行所帶來的經濟後果所致 (這些應對方法包括用以維持勞動力隊伍的經濟援助、和使商業模式能繼續生存的財政支援) 。

2019冠狀病毒病危機揭示了資訊與通訊科技是讓商業運作能繼續進行的關鍵技術。本研究分析資訊與通訊科技能力如何於危機發生時影響新生創業者的策略。我們的分析有其重要性，這是因為這些創業者把資源投入他們的新項目; 我們必須鑒定他們對危機所採取的戰略對策: 適應、賠償和脫離。取自大流行期間有關的樣本本身已能就危機如何影響新生創業者的商務決策、提供獨特的啟示。

This study aims to explore how sustainable business models (SBM) can be developed within agri-innovation systems (AIS) and emphasize an integration of the two with a systemic understanding for reducing food loss and value loss in postharvest agri-food supply chain.

This study conducted longitudinal qualitative research in a developing country with food loss challenges in the postharvest supply chain. This study collected data through multiple rounds of fieldwork, interviews and focus groups over four years. Thematic analysis and “sensemaking” were used for inductive data analysis to generate rich contextual knowledge by drawing upon the lived realities of the agri-food supply chain actors.

First, this study finds that the value losses are varied in the supply chain, encompassing production value, intrinsic value, extrinsic value, market value, institutional value and future food value. This happens through two cumulative effects including multiplier losses, where losses in one model cascade into others, amplifying their impact and stacking losses, where the absence of data stacks or infrastructure pools hampers the realisation of food value. Thereafter, this study proposes four strategies for moving from the loss-incurring current business model to a networked SBM for mitigating losses. This emphasises the need to redefine ownership as stewardship, enable formal and informal beneficiary identification, strengthen value addition and build capacities for empowering communities to benefit from networked SBM with AIS initiatives. Finally, this study puts forth ten propositions for future research in aligning AIS with networked SBM.

This study contributes to understanding the interplay between AIS and SBM; emphasising the integration of the two to effectively address food loss challenges in the early stages of agri-food supply chains. The identified strategies and research propositions provide implications for researchers and practitioners seeking to accelerate sustainable practices for reducing food loss and waste in agri-food supply chains.

This study seeks to examine the relationship between macroeconomic shock indicators, namely geopolitical risk (GPR), global economic policy uncertainty (GEPU) and financial stress (FS), and returns as well as volatilities on seven carefully selected stock markets in Africa. Specifically, the study intends to unravel the co-movement and interdependence between the respective macroeconomic shock indicators and each of the stock markets under consideration across time and frequency.

This study employed wavelet coherence approach to examine the strength and stability of the relationships across different time scales and frequency components, thereby providing valuable insights into specific periods and frequency ranges where the relationships are particularly pronounced.

The study found that GEPU, Financial Stress (FS) and GPR failed to induce significant influence on African stock market returns in the short term (0–4 months band), but tend to intensify in the long-term band (after 6th month). On the contrary, stock market volatilities exhibited strong coherence and interdependence with GEPU, FSI and GPR in the short-term band.

This study happens to be the first of its kind to comprehensively consider how the aforementioned macro-economic shock indicators impact stock markets returns and volatilities over time and frequency. Further, none of the earlier studies has attempted to examine the relationship between macro-economic shocks, stock returns and volatilities in different crisis periods. This study is the first of its kind in to employ data spanning from May 2007 to April 2023, thereby covering notable crisis periods such as global financial crisis (GFC) and the COVID-19 pandemic episodes.

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.

This study aims to explore the impact of systematic liquidity risk on the averaged cross-sectional equity return of the Indian equity market. It also examines the effects of illiquidity and decomposed illiquidity on the conditional volatility of the equity market.

The present study employs the Liquidity Adjusted Capital Asset Pricing Model (LCAPM) for pricing systematic liquidity risk using the Fama & MacBeth cross-sectional regression model in the Indian stock market from January 1, 2012, to March 31, 2021. Further, the study employed an exponential generalized autoregressive conditional heteroscedastic (1,1) model to observe the impact of decomposed illiquidity on the equity market’s conditional volatility. The study also uses the Ordinary Least Square (OLS) model to illuminate the return-volatility-liquidity relationship.

The study’s findings indicate that the commonality between individual security liquidity and aggregate liquidity is positive, and the covariance of individual security liquidity and the market return negatively affects the expected return. The study’s outcome specifies that illiquidity time series analysis exhibits the asymmetric effect of directional change in return on illiquidity. Further, the study indicates a significant impact of illiquidity and decomposed illiquidity on conditional volatility. This suggests an asymmetric effect of illiquidity shocks on conditional volatility in the Indian stock market.

This study is one of the few studies that used the World Uncertainty Index (WUI) to measure liquidity and market risks as specified in the LCAPM. Further, the findings of the reverse impact of illiquidity and decomposed higher and lower illiquidity on conditional volatility confirm the presence of price informativeness and its immediate effects on illiquidity in the Indian stock market. The study strengthens earlier studies and offers new insights into stock market liquidity to clarify the association between liquidity and stock return for effective policy and strategy formulation that can benefit investors.

Job satisfaction is essential in understanding turnover intentions. Previous studies reveal that highly educated hospitality employees generally have lower levels of job satisfaction, indicating that the antecedents of job satisfaction may be different from hospitality managers and frontline employees. This study compared the different antecedents of job satisfaction for housekeeping managers and employees.

This study used a mixed-methods approach for a two-part study. The researchers recruited housekeeping managers for the exploratory survey. The results of open-end questions helped us build a custom dictionary for the text mining of comments from Glassdoor.com. Finally, a multilinear regression of themes from housekeeping employees’ ratings on Glassdoor.com was conducted to understand the antecedents of job satisfaction for housekeeping managers and employees.

The results of the exploratory survey indicated that the housekeeping department has an urgent need for organizational support and training. The text-mining revealed organizational support impacts both managers and frontline employees, while training impacts managers more than employees. Finally, the regression analysis showed compensation, business outlook, senior management, and career opportunity impacted both groups. However, work-life balance only influenced managers.

With a large number of employees at low salaries, housekeeping departments have a higher-than-average turnover rate for lodging. This study is among the first to compare the antecedents of managers’ and frontline employees’ job satisfaction in the housekeeping department, extending Social Exchange Theory. It provides suggestions for the housekeeping department to decrease turnover intentions.