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Purpose: Innovation is a multi-dimensional phenomenon influenced at the organisational level by internal and external factors that can determine how innovative an organisation can be, determining a firm’s business performance. This chapter measures and predicts how innovative a company can be, considering key internal factors using modern data analytics/science.

Need for Study: The increasing challenge of modern business operations is affected by how quickly, sustainably, effectively, and efficiently companies can innovate to mitigate the dynamic challenges of current business environments and evolving customer needs. The ability to predict, measure, and manage innovation becomes necessary to ensure that businesses are fit for purpose.

Methodology: A model was designed following the study hypotheses and statistically tested. A historical data sample from the OECD global industry dataset for eight years was used for the analysis. The ordinary least square method was used to test for model fit. Also, in machine learning engineering, predictive analysis using the multivariate linear regression analysis method was carried out.

Findings: The results support the hypotheses that an organisation’s capacity to be innovative can be measured and predicted, and it is influenced by a good number of internal factors or independent variables at various degrees.

Practical Implications: Managers must understand how to measure and predict innovation metrics to manage innovation better, ultimately leading to better business outcomes and performance. Also proposed are new measurement matrices for innovation management: innovation capacity (I_C), business innovation value (BIV), innovation creation factor (ICF), and a practical data-driven innovation management and prediction system.

This chapter revisits the Hausman (1978) test for panel data. It emphasizes that it is a general specification test and that rejection of the null signals misspecification and is not an endorsement of the fixed effects estimator as is done in practice. Non-rejection of the null provides support for the random effects estimator which is efficient under the null. The chapter offers practical tips on what to do in case the null is rejected including checking for endogeneity of the regressors, misspecified dynamics, and applying a nonparametric Hausman test, see Amini, Delgado, Henderson, and Parmeter (2012, chapter 16). Alternatively, for the fixed effects die hard, the chapter suggests testing the fixed effects restrictions before adopting this estimator. The chapter also recommends a pretest estimator that is based on an additional Hausman test based on the difference between the Hausman and Taylor estimator and the fixed effects estimator.

The COVID-19 pandemic created a broad array of challenges for hospitals. These challenges included restrictions on admissions and procedures, patient surges, rising costs of labor and supplies, and a disparate impact on already disadvantaged populations. Many of these intersecting challenges put pressure on hospitals' finances. There was concern that financial pressure would be particularly acute for hospitals serving vulnerable populations, including safety-net (SN) hospitals and critical access hospitals (CAHs). Using data from hospitals in Washington State, we examined changes in operating margins for SN hospitals, CAHs, and other acute care hospitals in 2020 and 2021. We found that the operating margins for all three categories of hospitals fell from 2019 to 2020, with SNs and CAHs sustaining the largest declines. During 2021, operating margins improved for all three hospital categories but SN operating margins still remained negative. Both changes in revenue and changes in expenses contributed to observed changes in operating margins. Our study is one of the first to describe how the financial effects of COVID-19 differed for SNs, CAHs, and other acute care hospitals over the first two years of the pandemic. Our results highlight the continuing financial vulnerability of SNs and demonstrate how the factors that contribute to profitability can shift over time.

The traditional predictor of technical inefficiency proposed by Jondrow, Lovell, Materov, and Schmidt (1982) is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

Purpose: This chapter investigates the moderating impact of personality and demographic factors on the association between work–life balance (WLB) and the well-being (WB) of Ayurveda doctors in Sri Lanka.

Need for the Study: WB is necessary for everyone’s life. Individuals must meet proper WLB between their private and career life scenarios. On the other hand, employee WB and WLB are considered under the sustainable development goals. Hence, it is required to investigate the effect of WLB on WB.

Methodology: This quantitative, cross-sectional study was conducted with minimal researcher interference. The primary data were collected using structured questionnaires from Ayurvedic Doctors in Sri Lanka. The correlation, regression, and hierarchical regression analyses with multivariate assumptions were conducted using SPSS.

Findings: The findings reveal a robust positive association between the WLB and WB, indicating the same association between the WLB and personality. Moreover, there is a strong positive association between personality and WB. The results of the moderator analysis presented that there is a marginal moderator impact from the personality towards the association between WLB and WB.

Practical Implications: Ayurveda Practitioners and policymakers can use the generated knowledge in decision-making. The results of this study can be used as a reference by all industrial practitioners to improve their business practices. They can do this by raising employee WLB to enhance WB, which will help them keep the best employees within the company.

This study examined the factors influencing willingness-to-repurchase for each class of airline service, and integrate the constructs of service quality, satisfaction and willingness-to-repurchase which were rooted on Engel-Kollat-Blackwell (EKB) model. The study focuses on the domestic and international arrival of passengers at Murtala Muhammed International Airport in Lagos and Nnamdi Azikwe International Airport in Abuja. Information was gathered from domestic and foreign passengers who had post-purchase experience and had used the airline's services more than once. The survey data were obtained concurrently from arrival passengers at two major international airports using an electronic questionnaire through random and purposive sampling techniques. The data was analysed using the ordinal logit model and structural equation model. From the 606 respondents, 524 responses were received but 489 responses were valid for data analysis and reporting and were obtained mostly from economy and business class passengers. The study found that the quality of seat pitch, allowance of 30 kg luggage permission, availability of online check-in 24 hours before the departing flight, quality of space for legroom between seats, and the quality of seats that can be converted into a fully flatbed are the major service factors influencing willingness-to-repurchase economy and business class tickets. Also, it was found that passengers' willingness to repurchase is influenced majorly by service quality, but not necessarily influenced by satisfaction. These results reflect the passengers' consciousness of COVID-19 because the study was conducted during the heat of COVID-19 pandemic. Recommendations were suggested for airline management based on each class.

The current study aimed to explore the extent of accounting graduates’ abilities (AGAs) in Jordan to use modern information technology systems (MITSs) to make optimal financial decisions (OFDs). To carry out this study, the descriptive analytical approach was used to collect and analyze data. Accordingly, an electronic questionnaire was designed and distributed to 5,000 students in 30 universities in Jordan. However, only 1,067 questionnaires were valid for analysis, which constituted the sample of study. To test the research hypotheses, the descriptive analysis and multivariate regression test were used. The results indicated that the majority of accounting graduates (AGs) in Jordanian universities (JUs) do not realize the importance role of MITSs to make OFDs. In addition, the biggest challenge face AGs in JUs is the weakness of current accounting curricula in JUs in terms of not including materials related to MITSs and the role of these systems in performing accounting tasks. Finally, the study found that the current abilities of AGs in JUs moderate negatively and significantly the positive relationship between the use of MITSs and making OFDs. Accordingly, the study recommended that JUs work on developing accounting curricula through including modern courses related to the use of MITSs in performing accounting tasks.