A Recommendation System for People Analytics

Companies seek to leverage data and people analytics to maximize the business value of their talent. This article proposes a recommendation system for personalized workload assignment in the context of people analytics. The article describes the system, which follows a novel two-level hybrid architecture. We evaluate the system performance in a series of computational experiments and discuss future extensions. Overall, the proposed system could create significant business value as a decision support system that could help managers make better decisions. The article demonstrates how computational and machine learning approaches can complement humans in improving the performance of organizations.

An Empirical Investigation of Factors Determining Actual Usage of Entertainment Streaming Apps in India

Rise of internet and penetration of smartphones have made digital content accessible though Entertainment Streaming Apps (ESA). With the flexibility of time and place, ESA platforms are changing the dynamics of entertainment consumption. The current study explored the determinants of actual usage of ESA using the theory of planned behavior, flow theory and factors affecting entertainment related technology adoption including engagement, content, entertainment value, convenience value and monetary value. Data is collected through an online survey from 215 Indian ESA users and the proposed framework is empirically tested using partial least squares structural equation modeling (PLS-SEM). The findings of the study contribute to the growing body of literature on streaming apps adoption and usage by expanding the understanding of the factors that explain its usage behavior.

Modeling the Critical Success Factors for Business Intelligence Implementation

Business intelligence (BI) helps organizations to make better and quicker decisions. The primary requirement, as per previous studies, for any successful BI implementation in an organization and its' stakeholders, is to understand and pay heed to the vital issues and factors governing it. The objective of this study is, thus, to analyze the various critical success factors (CSF’s) for Business Intelligence Implementation, in context to the Indian sub-continent. In this qualitative study, the CSF’s for BI implementation are classified, through the review of the literature and to identify the relationship among the CSF's, Interpretive Structural Modeling (ISM) is applied along with MICMAC classification method. The ISM approach's outcome shows that management support and business goal alignment are the most significant driving factors for implementing BI. These findings may help recognize the crucial facts that affect the firms adopting BI in India and give some insights for other countries

Determine Factors of NFC Mobile Payment Continuous Adoption in Shopping Malls

Near Field Communication (NFC) mobile payment systems allow users to utilize services through smartphones. There is insufficient literature exploring the adoption of NFC with payment scenarios in developing countries. This study aims to explore the influential factors of consumer adoption of NFC, taking payment behaviors through NFC in Indonesia as an example. One hundred forty-seven participants were enrolled in the 5-point Likert scale survey, and 124 valid samples were analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that trust mediates the effect of context on consumers' continuous intention to use NFC mobile payment. Additionally, trust mediates the effect of perceived risk on consumers' continuous intention to use. The perceived ease of use and perceived usefulness have no effects on consumers' continuous intention to use. The mediating effect of religiosity has not been observed in this study. The findings can enbale service providers and local governments to offer better mobile payment services.

A Recommendation System for People Analytics

Companies seek to leverage data and people analytics to maximize the business value of their talent. This article proposes a recommendation system for personalized workload assignment in the context of people analytics. The article describes the system, which follows a novel two-level hybrid architecture. We evaluate the system performance in a series of computational experiments and discuss future extensions. Overall, the proposed system could create significant business value as a decision support system that could help managers make better decisions. The article demonstrates how computational and machine learning approaches can complement humans in improving the performance of organizations.

An Empirical Investigation of Factors Determining Actual Usage of Entertainment Streaming Apps in India

Rise of internet and penetration of smartphones have made digital content accessible though Entertainment Streaming Apps (ESA). With the flexibility of time and place, ESA platforms are changing the dynamics of entertainment consumption. The current study explored the determinants of actual usage of ESA using the theory of planned behavior, flow theory and factors affecting entertainment related technology adoption including engagement, content, entertainment value, convenience value and monetary value. Data is collected through an online survey from 215 Indian ESA users and the proposed framework is empirically tested using partial least squares structural equation modeling (PLS-SEM). The findings of the study contribute to the growing body of literature on streaming apps adoption and usage by expanding the understanding of the factors that explain its usage behavior.

Comparing requirements analysis techniques in business intelligence and transactional contexts

Requirements elicitation is a key concern in information technology (IT) projects. Busi-ness intelligence systems (BI) have emerged and are now used widely in organizations. These systems are designed to support manager's decision-making in their business performance moni-toring activities and their requirements are very different from those of transactional systems. But past research did not consider these differences. Therefore, this paper relies on a comparative approach to assess differences in the level of use and perceived effectiveness of requirements analysis techniques in both business intelligence and transactional contexts. An exploratory quali-tative study was conducted with two phases of semi-structured interviews with experienced practitioners. Our results show that 28% of the techniques differ in their level of use or perceived effectiveness, thus demonstrating the specificity of decision makers' needs. Our results reveal the importance of using techniques appropriate to the context to adequately define requirements and improve projects’ success.

Voice Engagement Leading to Business Intelligence

There are multiple studies establishing the importance of Business Intelligence (BI), in the Big Data Analytics context. Voice is yet to be seen as a contributing channel. Voice enabled assistants are at the forefront of conversational AI advancement. As humans speak to devices, brands and business are investing in engagement through voice channel. This voice engagement is resulting in both intangible and tangible benefits and generating voice commerce. The resultant voice data should be integral to BI, leading to Voice BI. This paper proposes a conceptual framework from engagement to intelligence, with support of five propositions to realise voice business intelligence. Type of applications and their engagement characterisation is segregated to create better understanding using Cross-Cases Observation Technique. Along with future research agenda to strengthen the propositions, this investigation observes building voice business intelligence by tracking relevant metrics which enable informed decisions.

Employer Brand Equity and Its Impact on the Application Intent of the Prospective Employees

Skilled human capital marks organisational success. Attracting, recruiting, and retaining these employees is vital. Companies are applying branding principles to recruitment function. This is termed as employer branding (EB). Identifying a right fit to suit organizational needs is essential. Organizational communication plays a critical role to relate to job seekers of generation-i; they rely on information imparted via various media and decode their perception. In this study, it is evident that for job seekers, corporate websites are important sources of information while deciding their employer. The reviews on job hunting sites aid perception. Use of AI to relate to prospective employees adds competitive edge. This study was conducted to ascertain dimensions of EB and study the application intent of potential employees. It included students from engineering and management and the employees from the job market. The findings allowed comparison of application intent of three groups. Qualification and experience moderates' application intent of prospective employees was evident.

Cyber Security Solutions for Businesses in Financial Services

This paper examines the challenges that small, medium, and large businesses in the financial services industry are facing concerning data security and providing relevant tools and strategies to protect the same. A qualitative research-based approach has been used where one-on-one interviews were conducted with 10 CIOs (chief information officers) and CISOs (chief information security officers). This data was compared with secondary data sources to validate the findings. This paper presents an in-depth analysis regarding security technologies and their efficacy to protect data assets and sensitive information. It will also opine about the technologies that each business type can use economically to cover the gamut of cyber-attacks. Existing research is restricted to either addressing small and medium businesses (SMBs) or large businesses. This paper attempts a comprehensive review for all sizes of businesses.