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The purpose of this paper is to develop a new comprehensive metric to successfully plan and execute IT projects. The development will be based on a study of all the variables that go into making a successful IT project.

A questionnaire, containing qualitative and quantitative response questions, to gather data from practicing project managers is designed and used in an IT company. Cronbach’s alpha is used to analyze the data and multiple regression is used to find the equation relating project success to project management success.

A comprehensive variable called Project Health Index (PHI) has been identified. Using this variable, one can predict whether a project is likely to succeed or not. This comprehensive, composite variable is calculated by using 17 other project-related metrics identified from the responses to the questionnaire.

The PHI has been calculated for the company studied. However, more studies need to be performed before it can be established that the PHI can also be used in other companies and projects. What has been established and validated is that PHI can be used in the studied company and that the methodology to calculate PHI is valid.

The PHI can be used as a predictive variable, i.e. one that can be used to take corrective and preventive actions to make a project successful. The PHI can also be used to allocate resources, prioritize the allocation and improve project management during the course of project execution.

By implementing projects efficiently, resource utilisation increases and leads to waste avoidance. Improved sustainability is the end result.

The work is original. The contents and the conclusions drawn, as well as the use of the PHI will enable IT companies to implement projects efficiently, reduce cost and enhance profit.

This study illustrates the use of the repertory grid in identifying and assessing effective teaching competencies.

The data were collected from two subsamples that consist of business management students and engineering students. The systematic repertory grid (RepGrid) method was performed to identify broad effective teaching competencies.

Broad effective teaching competencies found in the study include teaching approach, behavioral orientation, subject expertise, and communication skills among others.

Interestingly, the responses of the subsamples differ in the competencies identified and the weights assigned to a particular competency. Further, the results indicate the importance of having a “context” and thereby challenge the concept of generic teaching competencies.

The universities are encouraged to use RepGrid technique to assess effective teaching competencies of their faculty members.

The techniques for developing teaching competency models by some prior studies have several inherent flaws including the efficiency and effectiveness of data collection. The study takes forward the suggestions of scholars to use a rigorous technique, repertory grid, to overcome several of these flaws to a large extent.

A composite sentiment index (CSI) from quantitative proxy sentiment indicators is likely to be a lag sentiment measure as it reflects only the information absorbed in the market. Information theories and behavioral finance research suggest that market prices may not adjust to all the available information at a point in time. This study hypothesizes that the sentiment from the unincorporated information may provide possible market leads. Thus, this paper aims to discuss a method to identify the un-incorporated qualitative Sentiment from information unadjusted in the market price to test whether sentiment polarity from the information can impact stock returns. Factoring market sentiment extracted from unincorporated information (residual sentiment or sentiment backlog) in CSI is an essential step for developing an integrated sentiment index to explain deviation in asset prices from their intrinsic value. Identifying the unincorporated Sentiment also helps in text analytics to distinguish between current and future market sentiment.

Initially, this study collects the news from various textual sources and runs the NVivo tool to compute the corpus data’s sentiment polarity. Subsequently, using the predictability horizon technique, this paper mines the unincorporated component of the news’s sentiment polarity. This study regresses three months’ sentiment polarity (the current period and its lags for two months) on the NIFTY50 index of the National Stock Exchange of India. If the three-month lags are significant, it indicates that news sentiment from the three months is unabsorbed and is likely to impact the future NIFTY50 index. The sentiment is also conditionally tested for firm size, volatility and specific industry sector-dependence. This paper discusses the implications of the results.

Based on information theories and empirical findings, the paper demonstrates that it is possible to identify unincorporated information and extract the sentiment polarity to predict future market direction. The sentiment polarity variables are significant for the current period and two-month lags. The magnitude of the sentiment polarity coefficient has decreased from the current period to lag one and lag two. This study finds that the unabsorbed component or backlog of news consisted of mainly negative market news or unconfirmed news of the previous period, as illustrated in Tables 1 and 2 and Figure 2. The findings on unadjusted news effects vary with firm size, volatility and sectoral indices as depicted in Figures 3, 4, 5 and 6.

The related literature on sentiment index describes top-down/ bottom-up models using quantitative proxy sentiment indicators and natural language processing (NLP)/machine learning approaches to compute the sentiment from qualitative information to explain variance in market returns. NLP approaches use current period sentiment to understand market trends ignoring the unadjusted sentiment carried from the previous period. The underlying assumption here is that the market adjusts to all available information instantly, which is proved false in various empirical studies backed by information theories. The paper discusses a novel approach to identify and extract sentiment from unincorporated information, which is a critical sentiment measure for developing a holistic sentiment index, both in text analytics and in top-down quantitative models. Practitioners may use the methodology in the algorithmic trading models and conduct stock market research.

This study aims to uncover and develop explicit knowledge of existing smart communities (SCs) to guide services and business solutions for enterprises and serve community users in a well-thought-out manner. These sagacious frameworks will assist in analyzing trends and reaching out to pre-existing setups with different degrees of expertise.

A systematic overview is provided in this paper to unify insights and competencies toward building SCs; a hybrid analytical approach is used consisting of machine learning and bibliometric analysis. Scopus and Web of Science (WoS) are the primary databases for this purpose.

SCs implement cutting-edge technologies to enhance mobility, elevating information and communication technology (ICT) skills and data awareness while improving business processes and efficiency. This system of SC is an evolution of the conventional method. It provides a foundation for intelligent community services based on individual users and technologies such as the Internet of Things (IoT), artificial intelligence, cloud computing and big data. Manufacturing-based, service-based, retail-based, resource management and infrastructure-based SCs exist in the literature.

The paper summarizes a conceptual framework of SCs based on existing works around SCs. To the best of the authors’ knowledge, this is the first systematic literature review that uses a hybrid approach of topic modeling and bibliometric analysis to understand SCs better.

This study extends the literature on the effectiveness of e-learning by investigating the role of student engagement on perceived learning effectiveness (PLE) in the context of Indian higher education. Further, the impact of personal factors (Internet self-efficacy (ISE)) and environmental factors (information, system and service quality parameters) on various dimensions of student engagement (behavioral, emotional and cognitive) is studied through the lens of social cognitive theory (SCT).

An online management information systems (MIS) course is delivered to a batch of 412 postgraduate students. An online survey was conducted to measure the factors affecting their PLE. In addition to the survey, a summative assessment is conducted to evaluate the students in terms of their marks to assess their achievements (actual learning). Covariance-based structural equation modeling (CB-SEM) is used to validate the developed research model.

It is discovered that the IS (information system) quality parameters (environmental factors) positively impact PLE. The ISE affects the PLE through the mediating effect of all the dimensions of student engagement. Furthermore, there exists a positive relationship between PLE and student marks.

This study develops a research model using personal and environmental factors to understand PLE through the lens of SCT and then empirically validates it. The psychological process from the students' ISE to the PLE is explained through the mediating effects of various dimensions of engagement. Further, it is found that the PLE is positively related to student marks.

The new coronavirus is a highly infectious disease with mutating variants leading to pervasive risk around geographies and public health system. The economy has been suffering due to the strategic lockdown adopted by the local administrative bodies, and in most of the countries, it is further leading to a major wave of unemployment with millions of job and business losses affecting the hotels, travel and tourism industry widely. To attain a sustainable business in the post-pandemic situations, the industry now must think of information system approaches to convince tourists to feel safe with the most hygienic hospitality and services to be offered in any property. The key aspect of the study is to provide the impact of new-age AI-driven technology solutions that will dominate the future direction of the modernized hospitality industry promising robust health-safety measures in a hotel, and further help create sustainable business and leisure travel facilities to cope with post-epidemic scenarios.

The study emphasizes to provide a robust technology-oriented framework based on a mixed research method that would help hotels to adopt and implement new-age AI-driven solution within the hotel premise to serve customers with at most hygiene, contactless service and thereafter, aiming for faster recovery of businesses and regaining customer trust to fuel booking intent in the post-epidemic scenario.

The paper provides a technology-focused solution that would impact hotel industries' post-pandemic scenario. The study contributes to helping boost the tourism industry using information management solutions such as biosensors, robotic room services and contactless hosting. The findings show the adoption of robots/RPA solutions and Biosensors by the industry will be a disruptive paradigm shift.

The study expands the scope of research in information technology and management with a focus on the hospitality industry while contributing to new factors impacting customer buying behavior in the industry.

The COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most countries across the world have been forced to order partial or complete shutdown of their economies for a period of time to contain the spread of the virus. The fallout of this action manifested in loss of livelihood, migration of the labor force and severe impact on mental health due to the long duration of confinement to homes or residences.

The current study identifies the focus areas of the research conducted on the COVID-19 pandemic. Abstracts of papers on the subject were collated from the SCOPUS database for the period December 2019 to June 2020. The collected sample data (after preprocessing) was analyzed using Topic Modeling with Latent Dirichlet Allocation.

Based on the research papers published within the mentioned timeframe, the study identifies the 10 most prominent topics that formed the area of interest for the COVID-19 pandemic research.

While similar studies exist, no other work has used topic modeling to comprehensively analyze the COVID-19 literature by considering diverse fields and domains.

This research aims to identify the key factors affecting Outbound Open Innovation between Startups and Big organizations using the multiple criteria decision-making analysis (MCDM) approach. The MCDM technique ranks the four key factors identified from the literature study that can help to improve collaboration opportunities with Startups.

Identification of key factors affecting Outbound Open Innovation between Startups and big organizations based on extant literature. A questionnaire is prepared based on these four identified key factors to gather views of the startup's employees, from the designer level to the startup's founder. MCDM techniques are used to evaluate the questionnaire. The ensemble technique is used to rank the key factors coming from three different MCDM methods.

The findings from the MCDM approach and Ensemble techniques give insight to the big organizations to facilitate outbound Open Innovation effectively. It also provides insight into the requirements of the startups and the kind of support they seek from the big organizations. The ranking can help the big organization close the gaps and make an informed decision to increase the effectiveness of the collaborations and boost innovation.

This is a unique research work where the MCDM approach is used to identify the ranking of key factors affecting outbound open innovation between startups and big organizations. The MCDM technique is followed by the ensemble method to rationalize the findings. Technology Relevance ranks highest, followed by Innovation Ecosystem, Organization commitment and Knowledge Sharing.

Businesses need to make quick decisions and adjustments to fulfill the growing online demand. Previous studies examined various factors affecting the online sales performance of products such as books, electronics and movies; however, they paid limited attention toward the local brand clothing products. The current study investigates the importance of different kinds of seller-generated and consumer-generated signals such as price, discount, product ratings, review volume, review sentiment, number of questions and interaction between some of these factors for predicting the sales performance of clothing products.

The multiple linear regressions has been employed to investigate the influence of various predictor variables on sales performance. The study also examines the importance of these predictor variables by using different machine learning models, including random forest (RF), neural networks and support vector regression (SVR).

The findings of the study emphasize the importance of price and discount rates offered on the product. The quantitative characteristics of reviews, such as review volume and average rating, have been found to be more important predictors than sentiment strengths. However, the sentiment strength of reviews with higher helpfulness scores plays a significant role in predicting sales performance.

The study highlights the varying importance of seller-based and consumer-based signals in predicting sales performance. It also investigates the interaction effect of these two kinds of signals. The consumer-generated signals have been further divided into two components based on social influence theory, and the interaction effects of these components have also been examined.