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This paper aims to investigate the firm performance of micro, small and medium enterprises (MSMEs) by using artificial intelligence-based supply chain resilience strategies. A theoretical framework shows the relationship between artificial intelligence, supply chain resilience strategy and firm performance.

A questionnaire is developed to survey the MSMEs of India. A sample size of 307 is considered for the survey. The employees working in MSMEs are targeted responses. The conceptual model developed is tested empirically.

The study found that eight hypotheses were accepted and two were rejected. There are five mediating variables in the current study. Artificial intelligence, the independent variable, positively affects all five mediators. Then, according to the survey and analysis of the final 307 responses from MSMEs, the mediating variables significantly impact the dependent variable, firm performance.

This study is limited to emerging markets only. Also this study used only cross sectional data collection methods.

This study is essential for supply chain managers and top management willing to adopt the latest technology in their organisation or firmfor a better efficient supply chain process.

This study investigated artificial intelligence-based supply chain resilience for improving firm performance in emerging countries like India. This study tried to fill the research gap in artificial intelligence and supply chain resilience.

This study aims to examine the relationship between cultural intelligence and organisational commitment and its effect on learning goal orientation and turnover intention within the expatriate society of the United Arab Emirates (UAE).

A survey instrument was developed to collect data from 173 non-management expatriates employed by multinational corporations located in Dubai, UAE. SmartPLS bootstrap software was used to analyse the path coefficients and test the research hypotheses.

The results demonstrate that cultural intelligence enhances both learning goal orientation and turnover intention of expatriates. Moreover, organisational commitment partially mediates the relationship between cultural intelligence and turnover intention/learning goal orientation.

This study contributes by advancing extant knowledge with regard to cultural intelligence and organisational commitment effects on turnover intention and learning goal orientation of expatriates within a context of high cultural heterogeneity.

A key challenge for exporters and international marketing/purchasing managers is formulating strategic responses to deal with geopolitical disruptions during a trade war between superpowers. While past studies provide insightful analysis of the influence of changes in the institutional environment (regulatory pressures) on national and firm-level trade activities, they tend to ignore the association between inward (sourcing) or outward (export) international activities of firms during a trade war. In this study, we aim to explore various strategic options employed by third-party SME exporters in response to geopolitical disruptions, institutional pressures and constraints during a trade war.

We adopted a qualitative methodology and applied a hermeneutical approach in collecting, analysing and theorising interview findings. We conducted interviews with 15 owners or senior managers from 12 Australian and New Zealand exporters that exported or sourced significantly from at least one party of the trade war, the USA or China, between 2018 and 2020.

Our study developed a typology of fencing vs. balancing for explaining third-party SME exporters’ response strategies in terms of export market and international sourcing locations during a trade war. Fencing strategy centres on location choice decisions based on a fence or a secure buffer zone. Balancing strategy focuses on leveraging opportunities outside the conflict zone, i.e. third-party countries. Our study finds that exporters’ location choice decisions are influenced by a number of institutional factors during the trade war.

Firstly, our study examined only the early phase of the trade war under the “Trump” era. Future research may consider a longitudinal study design that examines exporters’ responses to global political uncertainty over a longer term. Secondly, we chose Australia and New Zealand as the focal context of this study. Future research could investigate exporters from other third-party countries that have different institutional conditions during the US-China trade war.

Firstly, an exporting firm should monitor and assess closely the wider changes in international relations between their home country’s major security partner and major trading partner, and the impact of these changes on the political risks of operating in international locations. Secondly, as the trade war intensifies, the fencing option needs to be given a greater weight than the balancing option in the strategic decision making of an exporter from a third-party country. Lastly, we encourage marketers and managers to reflect on and differentiate short-term and long-term benefits in strategic market-sourcing location decisions.

Our study makes a pioneering effort to theorise the linkages between institutional factors and the combined evaluation of export market selection and sourcing location selection choices under global political uncertainty based on the institution-based view. We present a conceptual framework highlighting the importance of institutional avoidance, embeddedness, comparative institutional advantages and multiple institutional logics for SME exporters’ international location selections during the trade war. Furthermore, we combine these institutional factors into two overarching constructs namely institutional buffer and institutional pluralism.

An emerging research stream focuses on the place-based ecosystems where artificial intelligence (AI) innovations emerge and develop. This literature builds on the contextual turn in management research and, specifically, work on entrepreneurial ecosystems. However, as a nascent research area, the literature on AI and entrepreneurial ecosystems is fragmented across academic and practitioner boundaries and unconnected disciplines because of disparate and ill-defined concepts. As a result, the literature is disorganized and its main insights are latent. The purpose of this paper is to synthesize research on AI ecosystems and identify the main insights.

We first consolidate research on the “where” of AI innovation through a scoping review. To address the fragmentation in the literature and understand how entrepreneurial ecosystems are associated with AI innovation, we then use content analysis to explore the literature.

We identify the main characteristics of the AI and ecosystems literature and the key dimensions of “AI entrepreneurial ecosystems”: the local actors and factors in geographic territories that are coordinated to support the creation and development of AI technologies. We clarify the relationships among AI technologies and ecosystem dimensions and uncover the latent themes and underlying structure of research on AI entrepreneurial ecosystems.

We increase conceptual precision by introducing and defining an umbrella concept—AI entrepreneurial ecosystem—and propose a research agenda to spur further insights. Our analysis contributes to research at the intersection of management, information systems, and entrepreneurship and creates actionable insights for practitioners influenced by the geographic agglomeration of AI innovation.

Recognizing women’s potential and directing their talents to realize these potentials can be of great benefit. Accordingly, this paper aims to identify the characteristics of entrepreneurial intelligence in female entrepreneurs, drawing on a national-level study and the international literature on this topic.

The present paper conducted two studies. First, 15 female entrepreneurs in the Guilan province of Iran, who were selected using purposive sampling, were interviewed to identify the characteristics of entrepreneurial intelligence nationally. The data gathered by interviews were analyzed using inductive content analysis. Then, their validity was tested using qualitative validation and analyzed using Shannon entropy. In the second study, the characteristics of female entrepreneurial intelligence were identified through a qualitative metasynthesis. The results of the two studies were compared together.

This categorized entrepreneurial intelligence into six categories, namely, entrepreneurial insights, cognitive intelligence, social intelligence, intuitive intelligence, presumptuous intelligence and provocative intelligence. Ultimately the characteristics of women’s entrepreneurial intelligence in each category were compared according to the national-level study and the international literature.

This study has the potential to discover credible and robust approaches for further examining the contextualization of women’s entrepreneurial intelligence at both national and international levels, thereby advancing new insights. By conceptualizing various dimensions of entrepreneurial intelligence for the first time and exploring how contextual factors differ across nations and internationally for women’s entrepreneurship, this paper challenges the assumption that the characteristics of women’s entrepreneurial intelligence are uniform across the world.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

Nursing students encounter a combination of academic rigor, clinical demands and emotional hurdles. Juggling coursework, practical training and patient interaction can be stressful, and exposure to such situations may impact their psychological well-being. This study aims to highlight the top strengths among nursing students and identify the strengths associated with well-being.

Convenience sampling was used to select a sample of 150 nursing students studying in first, second and third year from colleges of Gujarat and Rajasthan. Students were administered the Values In Action character strengths inventory, the satisfaction with life scale and scale of positive and negative experience. Data was analyzed using descriptive statistics and correlation.

Results show that among nursing students, kindness emerged as the foremost strength with the highest mean, followed by honesty, creativity, spirituality and teamwork, and the strengths of curiosity, gratitude, perseverance, self-regulation, social intelligence, and zest were positively associated with life satisfaction and positive emotions and negatively related to negative emotions.

The small sample size was a limitation; however, this study has been conducted at different locations to improve generalizability.

This study has profound implications for nursing students, both in their personal development and their future roles as health-care professionals, as fostering these attributes can contribute to the students’ growth, well-being and effectiveness as compassionate and competent caregivers. Working on strengths is associated with well-being; therefore, using strengths identified by this study will have a beneficial effect on the students’ well-being.

Curiosity and social intelligence, for instance, can help nurses better understand patient needs and emotions, developing strengths like perseverance and self-regulation can equip nursing students with tools to cope effectively with the challenges inherent in health-care settings. Traits such as gratitude and social intelligence can enhance communication and empathy which are vital skills for establishing rapport with patients and their families. Emphasizing teamwork as a strength aligns with the collaborative nature of health care. By embodying values like kindness and spirituality, nursing students can create a more compassionate and meaningful experience for patients, as well as themselves.

The research paper identifies and emphasizes the five character strengths that are most commonly observed in a sample of Indian nursing students. In addition, this study delves deeper into these identified strengths to understand how they relate to the overall well-being of nursing students within this specific population. The existing literature has not explored it exhaustively.

This research investigates how to optimize storage location assignment to decrease the order picking time and the waiting time of orders in the staging area of low-temperature logistics centers, with the goal of reducing food loss caused by temperature abuse.

The authors applied ABC clustering to the products in a simulated database of historical orders modeled after the actual order pattern of a large cold logistics company; then, the authors mined the association rules and calculated the sales volume correlation indices of the ordered products. Finally, the authors generated three different simulated order databases to compare order picking time and waiting time of orders in the staging area under eight different storage location assignment strategies.

All the eight proposed storage location assignment strategies significantly improve the order picking time (by up to 8%) and the waiting time of orders in the staging area (by up to 22%) compared with random placement.

The results of this research are based on a case study and simulated data, which implies that, if the best performing strategies are applied to different environments, the extent of the improvements may vary. Additionally, the authors only considered specific settings in terms of order picker routing, zoning and batching: other settings may lead to different results.

A storage location assignment strategy that adopts dispersion and takes into consideration ABC clustering and shipping frequency provides the best performance in minimizing order picker's travel distance, order picking time, and waiting time of orders in the staging area. Other strategies may be a better fit if the company's objectives differ.

Previous research on optimal storage location assignment rarely considered item association rules based on sales volume correlation. This study combines such rules with several storage planning strategies, ABC clustering, and two warehouse layouts; then, it evaluates their performance compared to the random placement, to find which one minimizes the order picking time and the order waiting time in the staging area, with a 30-min time limit to preserve the integrity of the cold chain. Order picking under these conditions was rarely studied before, because they may be irrelevant when dealing with temperature-insensitive items but become critical in cold warehouses to prevent temperature abuse.

This paper aims to deal with the grape sourcing strategies of wineries in the Canary Islands.

Sourcing decisions are analysed from official registers of transactions between wineries and their external suppliers. The main sources of information are harvest reports submitted by wineries containing data about observable dimensions of their purchasing decisions. The general behaviour in the wine-grape zones that make up the grape market in the Canary Islands is described, and different strategies of individual wineries are revealed. Grape purchasing decisions are interpreted in terms of the potential explanatory factors involved in the undeclared objectives of wineries’ sourcing strategies. Two research questions are considered in this study: the spatial dimension, which refers to plot location, and the social dimension, which refers to the relationships between wineries and winegrowers.

The location of grape producers is a key factor in achieving the desired wine quality for wineries. The sourcing strategy of wineries is also influenced by size, but the impact of size varies depending on the short and long-term objectives of wineries.

Typically the literature on grape sourcing strategies relies on interviews with winemakers. However, this paper analyses wineries’ sourcing decisions based on records and reports that reveal their decisions in the specific context of the Canary Islands.

With the advancement of digital transformation, it is important for e-retailers to use artificial intelligence (AI) for customer engagement (CE), as CE enables e-retail brands to succeed. Essentially, AI e-marketing (AIeMktg) is the use of AI technological approaches in e-marketing by blending customer data, and Retail 4.0 is the digitisation of the physical shopping experience. Therefore, in the era of Retail 4.0, this study investigates the factors influencing the use of AIeMktg for transforming CE.

The primary data were collected from 305 e-retailer customers, and the analysis was performed using a quantitative methodology.

The results reveal that AIeMktg has tremendous applications in Retail 4.0 for CE. First, it enables marketers to swiftly and responsibly use data to anticipate and predict customer demands and to provide relevant personalised messages and offers with location-based e-marketing. Second, through a continuous feedback loop, AIeMktg improves offerings by analysing and incorporating insights from a 360-degree view of CE.

The main contribution of this study is to provide theoretical underpinnings of CE, AIeMktg, factors influencing the use of AIeMktg, and customer commitment in the era of Retail 4.0. Subsequently, it builds and validates structural relationships among such theoretical underpinning variables in transforming CE with AIeMktg, which is important for customers to expect a different type of shopping experience across digital channels.