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This study examines the adoption of cotton combine services and its impact on farm technical efficiency in Kazakhstan and Uzbekistan. The research aims to determine whether mechanisation influences productivity and economic output at the farm level.

Using farm-level data from 511 cotton growers in Kazakhstan and Uzbekistan collected in 2019, this study employs stochastic frontier analysis to measure potential output and technical inefficiency among cotton farmers. The analysis includes a translog functional form to account for the use of cotton combine services and other farming variables.

The findings indicate that while mechanisation through cotton combines can potentially increase technical efficiency by optimising the harvesting process, the benefits are not uniformly experienced across all farms. Variations in farm characteristics, such as labour availability and existing agricultural practices, influence the efficiency of technology adoption. Institutional factors and historical legacies also play a significant role in the adoption and impact of mechanisation.

The study is based on cross-sectional data from 2019, and the findings may not capture longer-term trends or recent developments in mechanisation policies in the study countries.

This research provides a nuanced understanding of the conditions under which cotton combine services enhance or hinder technical efficiency. It highlights the necessity for carefully tailored policies for mechanisation, especially in Uzbekistan, where rural labour is abundant and predominantly female. The study contributes to the broader discourse on agricultural mechanisation in developing countries by focusing on the specific context of Central Asia.

This article aims to evaluate published food cold chain (FCC) literature against risk management and supply chain sustainability concepts.

The article uses the theory refinement logic proposed by Seuring et al. (2021) to analyze the contents of FCC management-related literature published over the past 20 years. A sample of 116 articles was gathered using Web of Science and subsequently analyzed. The respective articles were then systematically coded against the frameworks of Beske and Seuring (2014) and Vlajic et al. (2012), which focused on building sustainable and robust supply chains, respectively.

The literature review revealed that debates around managing contemporary sources of disruptions/vulnerability and making FCCs more sustainable and resilient are gradually developing. However, an overarching risk management perspective along with incorporating social and environmental dimensions in managing FCCs still needs the adequate attention of the respective research community.

The deductive internal logic of theory refinement approach used in this paper could have been further strengthened by using additional frameworks. This limitation, however, opens avenues for further research. The findings of the paper will stimulate the interest of future researchers to work on expanding our understanding related to sustainability and risk management in FCCs.

The paper is the first attempt to organize published FCC literature along dimensions of supply chain sustainability and risk management. The paper thus provides the respective researchers with a foundation that will help them adopt a focused approach to addressing the research gaps.

This study aims to examine whether and how the presence of co-opted directors (directors appointed after the incumbent CEO) influences corporate climate risk disclosure.

This study comprehensively analyses 2,975 firm-year observations of US-listed companies, using ordinary least squares with industry and year-fixed effects. To confirm the reliability of the study results, the authors used several techniques, including propensity score matching, to address potential issues with functional form misspecification, analysed a subset of companies where co-option persisted over two consecutive years to mitigate concerns regarding reverse causality and difference-in-differences estimation, using the cheif executive officer’s (CEO’s) sudden death as an exogenous shock to board co-option to mitigate endogeneity concerns.

The findings indicate that the presence of a large number of co-opted directors negatively influences corporate climate risk disclosure. Mediation analysis suggests that managerial risk-taking partially mediates this negative association. Moderation analyses show that the negative impact of co-opted directors on climate risk disclosure is more pronounced in firms with greater linguistic obfuscation, limited external monitoring and in environmentally sensitive industries. Moreover, co-opted directors intentionally withhold or obscure the disclosure of transition climate risks more than physical climate risks.

This research has important implications for policymakers, regulators and corporate governance practitioners in designing board structures by highlighting the adverse impact of co-opted directors in contexts with lax regulatory enforcement and managerial discretion. The authors caution against relying on such directors for providing climate-related risk disclosures, especially in companies with poor external monitors and based in environmental sensitivities, as their placement can significantly undermine transparency and accountability.

This study adds to the existing body of knowledge by highlighting the previously unexplored phenomenon of intentional obscurity in disclosing climate risks by co-opted directors. This research provides novel insights into the interplay between board composition, managerial risk-taking behaviour and climate risk disclosure. The findings of this study have significant implications for policymakers, regulators and corporate governance experts, and may prompt a re-evaluation of strategies for improving climate risk disclosure practices.

This study explored use of big data analytics (BDA) to analyse data of a large number of construction firms to develop a construction business failure prediction model (CB-FPM). Careful analysis of literature revealed financial ratios as the best form of variable for this problem. Because of MapReduce’s unsuitability for iteration problems involved in developing CB-FPMs, various BDA initiatives for iteration problems were identified. A BDA framework for developing CB-FPM was proposed. It was validated by using 150,000 datacells of 30,000 construction firms, artificial neural network, Amazon Elastic Compute Cloud, Apache Spark and the R software. The BDA CB-FPM was developed in eight seconds while the same process without BDA was aborted after nine hours without success. This shows the issue of not wanting to use large dataset to develop CB-FPM due to tedious duration is resolvable by applying BDA technique. The BDA CB-FPM largely outperformed an ordinary CB-FPM developed with a dataset of 200 construction firms, proving that use of larger sample size with the aid of BDA, leads to better performing CB-FPMs. The high financial and social cost associated with misclassifications (i.e. model error) thus makes adoption of BDA CB-FPMs very important for, among others, financiers, clients and policy makers.

This exploratory inquiry sheds light on the nature of victim (i.e. provocative and passive) and specific work context in shaping the perceived causes and outcomes of felt workplace ostracism in teaching faculty of Pakistani higher educational institutions (HEIs) based on target-centric victimization framework.

This phenomenological research is based on data gathered from 30 ostracized teaching faculty members working in Pakistani public and private HEIs through in-depth semi-structured interviews. The interviews were tape-recorded, and transcription was entered in NVivo 12 Plus software to conduct thematic analysis.

This study found that provocative and submissive victim status, as well as the specific contextual factors in Pakistani HEIs (i.e. negative competition, cronyism, egoism and poor interpersonal relationships), is responsible for fostering workplace ostracism and yielding unique outcomes in each case.

This study has taken the scantly used target-centric victimization framework to distinguish the causes and consequences of workplace ostracism based on the nature of victim and work context in Pakistani HEIs .

In modern times, innovation is considered as a vital component of sustainable competitive advantage. The purpose of this paper is to identify how innovation at the individual level [innovative work behavior (IWB)] and at the organizational level [innovative organizational climate (IOC)] affects the chances of success of a particular project. Additionally, the moderating effect of gender and work culture on the relation between innovative climate and behavior is tested in the study.

Survey technique was used to collect data from 425 employees working in project departments at the executive, middle level and senior level management in the paint manufacturing industry of Pakistan. Multiple regression, as well as Preacher and Hayes (2004) tests, were applied to test the hypotheses.

The result of the data analysis showed that IWB acts as a mediator between IOC and project success (PS), thereby supporting the hypothesized model of innovation and PS. Work culture was supported as a moderator; however, no moderating effect of gender was validated by the results.

The management must make sure that to maximize the rate of success of projects, innovative work climate within the organizations and departments be given due importance. In addition to this, personnel’s individual innovation capabilities must also be enhanced by taking steps toward improvement through training and development.

Though attention has been given to research in innovation in light of other related variables, its relation to PS remains yet to be studied. The effect of gender and work culture on innovation in Pakistani paint industry was long over-due which has been addressed by this study.

Pakistan has marvellous growth and development capacity, and entrepreneurial small- and medium-sized enterprises (SMEs) play a key role in its development. As it is believed that entrepreneurial SMEs make an important contribution to the economy by providing employment and soaring production capacity, the purpose of this study is to investigate the hurdles that affect their performance.

A questionnaire-based survey was used to collect data from 225 entrepreneurial SMEs owners and managers in the southern region of Pakistan. Statistical analysis in SPSS was conducted to determine the barriers that limit the growth and development of SMEs.

The findings show that factors affecting entrepreneurial SMEs’ performance in Pakistan are lack of finance and infrastructure as well as economic barriers, corruption and management issues. These obstacles are positively and significantly associated with entrepreneurial SMEs’ failure.

This study’s significance lies in its identification of the barriers that affect the performance of entrepreneurial SMEs in Pakistan. The author also suggests how policymakers can devise better policies for SME growth.

Mobile phone-based human activity recognition (HAR) consists of inferring user’s activity type from the analysis of the inertial mobile sensor data. This paper aims to mainly introduce a new classification approach called adaptive k-nearest neighbors (AKNN) for intelligent HAR using smartphone inertial sensors with a potential real-time implementation on smartphone platform.

The proposed method puts forward several modification on AKNN baseline by using kernel discriminant analysis for feature reduction and hybridizing weighted support vector machines and KNN to tackle imbalanced class data set.

Extensive experiments on a five large scale daily activity recognition data set have been performed to demonstrate the effectiveness of the method in terms of error rate, recall, precision, F1-score and computational/memory resources, with several comparison with state-of-the art methods and other hybridization modes. The results showed that the proposed method can achieve more than 50% improvement in error rate metric and up to 5.6% in F1-score. The training phase is also shown to be reduced by a factor of six compared to baseline, which provides solid assets for smartphone implementation.

This work builds a bridge to already growing work in machine learning related to learning with small data set. Besides, the availability of systems that are able to perform on flight activity recognition on smartphone will have a significant impact in the field of pervasive health care, supporting a variety of practical applications such as elderly care, ambient assisted living and remote monitoring.

The purpose of this study is to build and test an accurate offline model by using only a compact training data that can reduce the computational and memory complexity of the system. This provides grounds for developing new innovative hybridization modes in the context of daily activity recognition and smartphone-based implementation. This study demonstrates that the new AKNN is able to classify the data without any training step because it does not use any model for fitting and only uses memory resources to store the corresponding support vectors.