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Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it difficult to forecast accurately. The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other traditional time-series methods.

In this study, long short-term memory (LSTM) networks are implemented to forecast container throughput. The container throughput data of the Port of Singapore are used for empirical analysis. The forecasting performance of the LSTM model is compared with seven different time-series forecasting methods, namely, autoregressive integrated moving average (ARIMA), simple exponential smoothing, Holt–Winter’s, error-trend-seasonality, trigonometric regressors (TBATS), neural network (NN) and ARIMA + NN. The relative error matrix is used to analyze the performance of the different models with respect to bias, accuracy and uncertainty.

The results showed that LSTM outperformed all other benchmark methods. From a statistical perspective, the Diebold–Mariano test is also conducted to further substantiate better forecasting performance of LSTM over other counterpart methods.

The proposed study is a contribution to the literature on the container throughput forecasting and adds value to the supply chain theory of forecasting. Second, this study explained the architecture of the deep-learning-based LSTM method and discussed in detail the steps to implement it.

Container throughput forecasting plays a pivotal role in strategic, tactical and operational level decision-making. The determination and analysis of the influencing factors of container throughput are observed to enhance the predicting accuracy. Therefore, for effective port planning and management, this study employs a deep learning-based method to forecast the container throughput while considering the influence of economic, environmental and social factors on throughput forecasting.

A novel multivariate container throughput forecasting method is proposed using long short-term memory network (LSTM). The external factors influencing container throughput, delineated using triple bottom line, are considered as an input to the forecasting method. The principal component analysis (PCA) is employed to reduce the redundancy of the input variables. The container throughput data of the Port of Los Angeles (PLA) is considered for empirical analysis. The forecasting accuracy of the proposed method is measured via an error matrix. The accuracy of the results is further substantiated by the Diebold-Mariano statistical test.

The result of the proposed method is benchmarked with vector autoregression (VAR), autoregressive integrated moving average (ARIMAX) and LSTM. It is observed that the proposed method outperforms other counterpart methods. Though PCA was not an integral part of the forecasting process, it facilitated the prediction by means of “less data, more accuracy.”

A novel deep learning-based forecasting method is proposed to predict container throughput using a hybridized autoregressive integrated moving average with external factors model and long short-term memory network (ARIMAX-LSTM).

The literature on Maritime Transportation (MT) is experiencing a transition phase where the focus of the research is repositioning. It registered steep growth in recent years with its beginning articles on the concepts of cost minimization to the current focus on achieving sustainable operational effectiveness using Information and Communication Technologies (ICTs). Thus, this becomes a right time to investigate the trajectory of research on MT.

The proposed study aims to explore the potential of data analytics techniques such as data mining and network analytics to reflect the trajectory of research in the maritime supply chain over time. This study identifies the eight main dimensions of the research published under maritime paradigm through network analytics. The in-depth review of these dimensions rendered us to segregate them further into sub-dimensions for the ease of understanding and interpretability. Further, the text mining is employed to extract thematic evolution of the research.

The evolved themes are completely exclusive from the conventional MT research with artificial intelligence, digital storage, waste management and biofuels emerging as contemporary themes. It is found that although there are a sufficient amount of literature on sustainable port practices but their policy implications are still underexplored. The inter-dimension research is needed to achieve the motive of economic efficiency and environmental sustainability simultaneously.

The study has contributed on the methodology side of conducting literature reviews. The dimensions, sub-dimensions and themes are obtained using data analytics tools and techniques. This omits the possibility of personal bias and thus making the results verifiable.

To understand: – the demand and supply side challenges in launching a new product in sports. – Factors, which go into the making of a successful “new” sport. – The role of planning in sport management.

In July 2017, on the eve of Pro Kabaddi League (PKL) Season 5, kabaddi had emerged as one of India’s most important non-cricketing sport. PKL was India’s first men’s professional kabaddi league, introduced by Mashal Sports and Star India in 2014. Kabaddi was an indigenous sport, and India had an unbeaten international track record as world champions. Yet, the sport and its players had never received their due in India. In 2017, while kabaddi’s popularity had increased, leading to sponsorship opportunities, huge player bids, prize money and television viewership, all was not quite hunky-dory. A women’s kabaddi league introduced only the previous year had not been continued, despite an extended format in 2017. The audience profile also did not match that envisaged by Star. As a unique creator of sports content, Star was in an enviable position in India; and so was Kabaddi as a sport. How had Star created a new property around an indigenous sport with rural and rustic associations, transforming it into a snazzy, up-market sport within just three years, even while leagues involving other popular sports failed to create a mark? Could Star sustain this interest? How could kabaddi retain its “star” position within Star’s stable?

In an undergraduate or a postgraduate programme in business administration.

Teaching notes are available for educators only.

CSS 11: Strategy.

The present study assesses the commercial bank profit efficiency and its relationship to banking sector competition in a rapidly growing emerging economy, India from 2009 to 2019 using stochastic frontier analysis (SFA).

Lerner indices, conventional and efficiency-adjusted, quantify competition. Two SFA models are employed to calculate alternative profit efficiency (inefficiency) scores: the two-step time-decay approach proposed by Battese and Coelli (1992) and the recently developed single-step pairwise difference estimator (PDE) by Belotti and Ilardi (2018). In the first step of the BC92 framework, profit inefficiency is calculated, and in the second step, Tobit and Fractional Regression Model (FRM) are utilized to evaluate profit inefficiency correlates. PDE concurrently solves the frontier and inefficiency equations using the maximum likelihood process.

The results suggest that foreign banks are less profit efficient than domestic equivalents, supporting the “home-field advantage” hypothesis in India. Further, increasing competition drives bank managers to make riskier lending and investment choices, decreasing bank profit efficiency. However, this effect varies depending on bank ownership and size.

Literature on the competition bank efficiency link is conspicuously scant, with a focus on technical and cost efficiency. Less is known regarding the influence of competition on bank profit efficiency. The article is one of the first to examine commercial bank profit efficiency and its relationship to banking sector competition. Additionally, the study work represents one of the first applications of the FRM presented by Papke and Wooldridge (1996) and the PDE provided by Belotti and Ilardi (2018).

Amidst the backdrop of a wide array of structural developments that have revolutionized the competitive landscape of Indian commercial banking, this paper aims to empirically examine the role of two external monitoring mechanisms – competition and concentration on financial stability and further highlights the significance of bank-level heterogeneity in the nexus.

The study uses the Lerner index, defined through a translog specification, as a measure of market power. A system generalized method of moments technique accounts for the dynamic associations among the competition-concentration-stability nexus. The study further examines the moderating effect of ownership, size and capitalization on the nexus. The study also uses the Boone indicator and comments on the competition-bank stability relationship after controlling for bank governance.

The findings indicate that banks are less stable in a more competitive and higher concentrated environment. Exploring bank-level heterogeneity, first, the authors report that as competition increases, state-owned banks have greater incentives to undertake risky activities than private and foreign banks, which point to implicit sovereign guarantees that characterize the former. Second, the authors document an adverse influence of competition on the soundness of larger banks consistent with the “too-big-to-fail” assertion. Third, results corroborate the disciplinary role of regulatory capital and lend support to stricter capital norms under Basel III in a more competitive environment.

This paper is perhaps the first to capture competition and concentration in a single model; to reconcile conflicting evidence on competition-risk nexus; to shed light on the joint effect of competition and Basel accords for Indian banks.

The purpose of this paper is to understand the deficiencies in Six Sigma project capability and empirically validating its impact on project success.

Deficiencies are identified from literature focusing on Six Sigma challenges or barriers. The study used a survey approach by circulating questionnaires to 400 Six Sigma professionals from 40 multinational organisations. The conceptual model is composed of 16 items measuring five constructs for hypothesis testing. Furthermore, structural equation modelling was used to identify the relationship between Six Sigma project capability deficiency and project success.

The findings of the study suggest that inadequate top management support, inadequate resources and change management and inadequate quality maturity form Six Sigma project capability deficiency and affect project success.

The study uses resource-based view to understand the deficiencies in Six Sigma project capability and their impact on project success. The study confirms that organisational capabilities in implementing Six Sigma affect the success of the projects.

The results of this study reveal that inadequate top management support, inadequate quality maturity of the organisation and inadequate resources and change management result into a deficiency in Six Sigma project capability. Quality managers in manufacturing and service organisations should attempt to improve these capabilities to achieve competitive advantage.

The study contributes to the literature by exploring the capability outlook of Six Sigma. The study attempts to fill the gap in Six Sigma literature by providing a structural model for understanding Six Sigma project capability deficiency and its impact on Six Sigma project success.

The purpose of this paper is to attempt to measure the performance of the Indian banking sector in terms of efficiency and productivity levels and their determinants during the post-reform period.

The present study is a novel attempt as it has used pooled data for a duration of 15 years (i.e. 1997/1998-2010/2011) from 59 selected banks for estimating the Hicks-Moorsteen (HM) total factor productivity (TFP) index.

Poor technical efficiency has experienced with scale efficiency change exerting dominant factors; whereas relatively better productivity growth has been experienced by the banks with major contributions from technical change components. The study found relatively underestimated efficiency and productivity levels by traditional data envelopment analysis-based Malmquist index. Additionally, the study brings into account the results for external and environmental determining factors contributing to the TFP growth.

Using HMTFP indices has helped to eliminate certain drawbacks of data envelopment and provided the more elaborative decomposition of productivity growth along with their components so as to have lucid and multidimensional insights about the performance of the Indian banking industry after the initiation of financial reforms.

The purpose of this study is to measure the level of total factor productivity of the Indian banking sector and to identify both the bank-specific and macroeconomic determinants of the total factor productivity after the global subprime mortgage crisis.

The research sample consists of 61 commercial banks including 21 public sector banks, 18 private sector banks and 22 foreign banks. The annual data is collected from the website of Reserve Bank of India from 2008 to 2019. The authors employed the non-parametric DEA approach to estimate Malmquist total factor productivity index for each bank as well as across different ownership groups. The panel data estimation technique was used to identify the determinants of total factor productivity.

The results suggested that an increase in the technological shift raised the bank's productivity above the optimal frontier. Among the bank-specific determinants, the bank size and bank diversifications are significantly declining productivity, whereas credit-deposit ratio and return on asset significantly increasing productivity. Among the macro-specific determinants, inflation, growth rate and fiscal deficit ratio negatively affect productivity, whereas capital formation to the GVA ratio boosts the level of productivity.

The authors have used intermediate method to select the inputs and outputs as per the suitability to the context. However, the disaggregate level such as state and district level analysis can be done using production and value-added approaches to explore the regional variations of the banking performance. Furthermore, the parametric methods such as stochastic frontier analysis can be used to examine banking performance, which the authors left for the future research.

This study suggested that banks should increase the economies of scale of their total assets and focus on the interest-earning activity. The banks need to proactively operate the business policy by following the changing path of inflation. The banks need to reduce their rate of fiscal-deficit to the GVA with the purpose to boost their level of productivity.

The study provides an important implication for bankers and policymakers in terms of heightening the banking performance during the period of dynamic economic events.

The purpose of this paper is to analyse the possible causes of elderly abuse in India and its repercussions for the society, based on the real cases and reports.

A multiple case study approach has been used for the study sourced from archival newspaper reports, crime reports, and narration.

Greater vigilance and more effective legislation would be required to solve the problem related to elder abuse.

There is not much study of causes, consequences, effectiveness of the legal system with respect to elderly abuse in India. In that way, it will be a unique contribution.