Impact of Rewards (Intrinsic and Extrinsic) on Employee Performance with Special Reference to Courier Companies of Faisalabad City

The study is conducted to measure the effect of rewards (Intrinsic & Extrinsic) on employee job performance. The experiences and personal opinions of employees working in different couriers companies were analyzed through questionnaires in the city of Faisalabad. The key objective of the study was to find that the rewards affect the performance of an employee. Statistical package (SPSS) is used for our study of descriptive analysis. Employees like Field Supervisors, Courier   from selected courier complainers (TCS, OCS, Leopard and Express Courier Services)   were randomly chosen. The focus of this study was to distribute the adequate level of incentives to the employees and create balance in distribution of rewards so that every employee contributes his efforts for the growth of the company. This study also focuses on two major rewards, intrinsic and extrinsic rewards. Rewards plays a motivational role in the personality of an employee and  urge them to  produce loyalty  and show good performance By the results study shows that  there is a strong relationship  between both type of  rewards and on  employee performance.  Concluding, this study has verified further research opportunities that can enhance the understanding of rewards and employees job performance. Keywords: Intrinsic Rewards, Extrinsic Rewards, Job Performance, TCS, OCS, Leopard & Express Couriers

An Extensive Review on Spectral Imaging in Biometric Systems: Challenges and Advancements

Spectral imaging has recently gained traction for face recognition in biometric systems. We investigate the merits of spectral imaging for face recognition and the current challenges that hamper the widespread deployment of spectral sensors for face recognition. The reliability of conventional face recognition systems operating in the visible range is compromised by illumination changes, pose variations and spoof attacks. Recent works have reaped the benefits of spectral imaging to counter these limitations in surveillance activities (defence, airport security checks, etc.). However, the implementation of this technology for biometrics, is still in its infancy due to multiple reasons. We present an overview of the existing work in the domain of spectral imaging for face recognition, different types of modalities and their assessment, availability of public databases for sake of reproducible research as well as evaluation of algorithms, and recent advancements in the field, such as, the use of deep learning-based methods for recognizing faces from spectral images

Breast Cancer Classification using Deep Learned Features Boosted with Handcrafted Features

Breast cancer is one of the leading causes of death among women across the globe. It is difficult to treat if detected at advanced stages, however, early detection can significantly increase chances of survival and improves lives of millions of women. Given the widespread prevalence of breast cancer, it is of utmost importance for the research community to come up with the framework for early detection, classification and diagnosis. Artificial intelligence research community in coordination with medical practitioners are developing such frameworks to automate the task of detection. With the surge in research activities coupled with availability of large datasets and enhanced computational powers, it expected that AI framework results will help even more clinicians in making correct predictions. In this article, a novel framework for classification of breast cancer using mammograms is proposed. The proposed framework combines robust features extracted from novel Convolutional Neural Network (CNN) features with handcrafted features including HOG (Histogram of Oriented Gradients) and LBP (Local Binary Pattern). The obtained results on CBIS-DDSM dataset exceed state of the art

The Impact of High Quality Relationship on Innovative Work Behavior of Employees through Psychological Wellbeing: A Case of Pharmaceutical Sector in Pakistan

The impact of qualities and experiences of high quality relationship is of great value to the companies and field of human resource. Pakistan is facing critical situation regarding qualities and experiences of relationship of employees. Innovative work behavior of employees has not been studied in developing countries like Pakistan. In order to assist the business in pharmaceutical sector this study is aimed at investigating the effect of experiences and qualities of high quality relationship on innovative work behavior of employees. Pharmaceutical sector was considered for this study as this sector has significant contribution in Gross Domestic Product (GDP) of Pakistan. This research was quantitative in nature in which data was collected from managerial and non-managerial employees of pharmaceutical sector. Total 310 questionnaires were completely filled and entered in SPSS for analysis. Correlation analysis was performed in SPSS to show relationship between the variables. Model was tested through structural equation modeling in AMOS and goodness of fit indices were estimated using Hu and Bentler (2010) criteria and all the values were found to show good fit model. To test the mediation among variables through regression the SOBEL test was used as a supplemental test. All the direct and mediational hypotheses were accepted. The results reveal that the psychological wellbeing mediates the relationship between experiences, qualities of high quality relationship and innovative work behavior. Limitation of the study and managerial implications are also discussed along with guideline for future research

The Innovative Business Model for Family-Owned Firms in the era of Digital Entrepreneurship: Evidence from Emerging Economy

The current Covid-19 pandemic has been changed the businesses plans. High uncertainty can compel the organization to change the business plan according to the market demand. In the current era of digitalization, organizations are needed to modify the existing business plan and innovate it through technologies. Modifying existing resources according to the market demand is challenging for the organization; employers face many challenges and obstacles. Businesses plan to develop a long-term business model to validate the attractiveness, reduce the avoidable investment of scarce resources, and structure the business process. In the current era of digitalization, businesses, specifically, SMEs cannot compete with the competitors who can adopt digitalization systems. Therefore, the current chapter is trying to find out the challenges faced by SMEs in developing economies during the adoption of the digital business model. In the current chapter, researchers focus on three different kinds of digital technologies that must be part of the business model during the era of digitalization, such as adopting digital technologies (artificial intelligence, Internet of Thing, and virtual reality and to create a new business model following the current era issue, these are the main block to resist of these market uncertainties in a new venture of family firms

MEASURING THE PERCEIVED EFFECTIVENESS OF ONLINE RECRUITMENT SYSTEM

This research aimed to examine the perceptions regarding the effectiveness of online recruitment in Pakistan by using the Technology Acceptance Model (TAM). Mixed-method research was conducted using qualitative and quantitative analyses. 30 HR mangers were interviewed and 170 HR personnel provided data via questionnaires. For qualitative data analysis Gioia, Corley, and Hamilton (2013) methodology was used, while quantitative data was analyzed using multiple regression. The results revealed that online recruitment facilitates organizations in terms of reaching wider applicants with less expenses and time consumption. Most importantly, HR managers and team members’ behavioral intentions are affected by key factors, including usefulness, technology self-efficacy, perceived ease of use, and attitude towards using websites.&nbsp

Parallel framework for earthquake induced response computation of the SDOF structure

Parallel computing briskly diminishes computation time through simultaneous use of multiple computing resources. In this research, parallel computing techniques have been developed to parallelize a program for obtaining a response of single degree of freedom (SDOF) structure under earthquake loading. The study uses Distributed Memory Processors (DMP) hardware architecture and Message Passing Interface (MPI) compilers directives to parallelize the program. The program is made parallel by domain decomposition. Concurrency in the program is created by dividing the program into two parts to run on different computers, calculating forced response and free response of the first half and the second half. Parallel framework successfully creates concurrency and finds structural responses in significant lesser time than sequential programs

Controllable Fano-type optical response and four-wave mixing via magnetoelastic coupling in a opto-magnomechanical system

We analytically investigate the Fano-type optical response and four-wave mixing (FWM) process by exploiting the magnetoelasticity of a ferromagnetic material. The deformation of the ferromagnetic material plays the role of mechanical displacement, which is simultaneously coupled to both optical and magnon modes. We report that the magnetostrictively induced displacement demonstrates Fano profiles, in the output field, which is well-tuned by adjusting the system parameters, like effective magnomechanical coupling, magnon detuning, and cavity detuning. It is found that the magnetoelastic interaction also gives rise to the FWM phenomenon. The number of the FWM signals mainly depends upon the effective magnomechanical coupling and the magnon detuning. Moreover, the FWM spectrum exhibits suppressive behavior upon increasing (decreasing) the magnon (cavity) decay rate. The present scheme will open new perspectives in highly sensitive detection and quantum information processing.Comment: 12 pages, 7 figure

In vitro evaluation of antimicrobial and cytotoxic potential of Epimedium grandiflorum hydroethanolic extract as natural medicine

Medicinal plants are used as fundamental and low-cost source for remedy of numbers of infectious and metabolic diseases in developing and developed countries. Current research work was planned to evaluate the antibacterial, antifungal, and cytotoxic potential of hydroethanolic extract of E. grandiflorum. It was found that selected natural medicinal herb have significant (p<0.05) antibacterial activities tested against Bacillus subtilis, Staphylococcus aureus, Pasteurella multocida, Escherichia coli, Klebsiella pneumoniae, Acinetobacter species, Pseudomonas Species and Salmonella Species. The results of bacterial biofilm inhibition also explored that selected natural herb has significant (p<0.05) capacity to prevent the microbial biofilm particularly at higher dose. The results of antifungal activities showed that selected medicinal plant has significant (p<0.05) antifungal potential evaluated against Aspergillus flavus, Aspergillus niger, Aspergillus terreus, Fusarium solani, Alternata alternaria, and Schizophyllum species. Moreover, the results of mutagenicity test and DNA damage preventive test explored that selective medicinal plant has significant (p<0.05) DNA protective capacity or in other words it is non-mutagenic or cytotoxic in nature. It could be concluded that E. grandiflorum could be a potential candidate as therapeutic agent to manage infectious diseases especial bacterial and fungal infections with non-toxic nature