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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.

Due to increase in operational risk, banks are facing huge losses. In order to avoid losses, banks need to manage operational risk. This study aims to analyze the impact of operational risk management (ORM) processes, which include identification, assessment, analysis, monitoring and control in the presence of corporate governance (CG) that can also contribute to effective ORM practices.

Operational risk management processes are used to manage operational risk along with CG. Primary data are collected through questionnaire from (167) operational risk managers of commercial banks. Multiple linear regressions has been run to analyze the data.

Results indicate significant impact of CG and operational risk identification (ORI), monitoring and control on ORM practices in commercial banks of Pakistan.

The study suggests policy makers to improve the ORM framework by CG. Beside this, in order to lessen operational risk, proper identification, monitoring and control of operational risk could also contribute.

With theoretical underpinnings in the conservation of resources theory, this research aims at understanding the link between workplace ostracism (WPO) and its effects on customers' interests in the context of COVID-19, with the mediation of stress and moderation of self-efficacy (SE).

The study followed a time-lagged design. A sample of 217 frontline employees working in the food sector of southern Punjab, Pakistan, responded to the study questions using the survey method with structured questionnaires. A Statistical Package for the Social Sciences (SPSS) tool was utilized for data analysis with bootstrapping and PROCESS macro.

The findings show that an important mechanism by which ostracism translates into customer service sabotage (CSS) is the increase in perceived stress levels of the employees. Additionally, SE was found to be an important personal resource that acts as a moderator in the said relationship.

Employees with high SE sense less workplace stress even during a pandemic. Leadership should consider the stress-alleviating effect of SE for lessening the damaging influence of WPO on customers.

The study fills an important empirical gap in the context of the COVID-19 pandemic, by showing that due to resource loss perceived by employees while being targeted by ostracism, they may decide to transfer their frustration towards organizational customers by sabotaging their service experience.