PurposeThis paper aims to analyze and give directions for advancing research in stock market volatility highlighting its features, structural breaks and emerging developments. This study offers a platform to research the benchmark studies to know the research gap and give directions for extending future research.Design/methodology/approachThe author has performed the literature review, and, reference checking as per the snowballing approach. Firstly, the author has started with outlining and simplifying the significance of the subject area, the review illustrating the various elements along with the research gaps and emphasizing the finding.FindingsThis work summarizes the studies covering the volatility, its properties and structural breaks on various aspects such as techniques applied, subareas and the markets. From the review’s analysis, no study has clarified the supremacy of any model because of the different market conditions, nature of data and methodological aspects. The outcome of this research work has delivered further magnitude to research the benchmark studies for the upcoming work on stock market volatility. This paper has also proposed the hybrid volatility models combining artificial intelligence with econometric techniques to detect noise, sudden changes and chaotic information easily.Research limitations/implicationsThe author has taken the research papers from the scholarly journal published in the English language only and the author may also consider other nonscholarly or other language journals.Originality/valueTo the best of the author’s knowledge, this research work highlights an updated and more comprehensive framework examining the properties and demonstrating the contemporary developments in the field of stock market volatility.

PurposeThis study aims to determine the number of segments of green consumer behavior on toiletries products, and the tendency of transition between clusters is estimated. This study also provides recommendations based on the results.Design/methodology/approachThis study used primary data collected through an online and offline questionnaire. The questionnaire was intended to identify the socio-demographic characteristics, green consumer behavior state according to the environment as well as the willingness of the respondents to purchase various toiletries products (current, less green, and greener). Prior to segmenting green consumer behavior, scale purification using confirmatory factor analysis was performed to ensure the indicators used were valid. The k-means clustering algorithm was used for the segmentation, while discriminant analysis was used to validate the segmentation result. The Markov chain approach was performed to estimate the tendency of the transition between constructed segments, where the logistic regression model was applied to predict the individual transition probability.FindingsThe clustering algorithm resulted in three segments: light green, green and dark green. The light green segment has the lowest attitude toward the environmental criteria while the members of the dark green segment have the highest attitude among the other segments. The logistic regression indicated that the tendency of individuals to stay in the current segment or move to the adjacent segment was influenced by socio-demographic factors. The one-step transition probability matrix revealed that the tendency of a particular segment to move to the greener segment was greater than to stay or even move to the less green segment. The Markov chain approach then showed that the steady-state condition will emerge after 18 steps.Research limitations/implicationsThis study was limited geographically and by the criteria used for segmenting the green consumer behavior; therefore, it is recommended that this study be replicated on a greater scale with more criteria. A wider geographic area could be considered, including a national study, and more criteria, such as social influences, could be considered. This study does not focus on specific toiletries products. Selecting more specific toiletries products could be considered to provide a more reliable response from the respondents. Moreover, factors around the willingness to pay for green products were not investigated in greater detail although these factors might become indicators that can distinguish between two or more segments.Practical implicationsThis study empirically supports the theory that consumer environmentally friendly behavior can be used to appropriately categorize consumers into several segments, and thereby guide the development of a more differentiated policy approach for business and government.Social implicationsGreen consumer behavior may help save the environment and it will be beneficial in reducing environmental damage.Originality/valueThe study extends the existing literature related to green consumer behavior by segmenting the green consumer behavior based on the environmental criteria and applying the Markov chain approach to estimate the tendency of transition between segments.

PurposeIn recent times, sustainable investment gaining much attention within the investors’ community and it is broadly driven by environmental, social and governance (ESG) factors. This study aims to examine the ESG-based sustainability index and economic policy uncertainty (EPU).Design/methodology/approachCorporate sustainability assessment procedure yields Dow Jones sustainability indexes (DJSIUS) and ESG compliant firms become a member of such indexes. To uncover the effects of policy uncertainty as follows: the study considers EPU index, equity market policy uncertainty index, economic and political events for the period 2000–2017. The authors present the study using a conditional volatility framework.FindingsThe correlation between the DJSIUS and policy uncertainty appears to be negative and statistically significant. It is apparent from the results that policy uncertainty does contain important ESG factors that explain the sustainable investment in US firms. Moreover, the stock market boom, credit crunch, Lehman collapse and fiscal crises have shown significant adverse effects on the sustainability index. More importantly, it is seen that investors’ sustainable investing considers presidential election years for portfolio planning; the uncertainty associated with the election years has also shown a negative impact on the sustainable returns.Practical implicationsFirst, sustainability is essential for the long-term stakeholders’ wealth maximization under governments’ policy uncertainty such as constrained resources, demographic and climate-change-policy, societal expectations, public-policies, regulatory structure. Second, EPU creates new opportunities and risks for sustainable firms and sustainable investing.Originality/valueThe study is novel in which the authors present the effects of uncertainty on socially responsible investing.

PurposeThis paper aims to study the concept, characteristics and factors of the resilient supply chain (RSC) and develop a hierarchical structural model and classify the factors based on their interrelationships.Design/methodology/approachThis paper has used a mixed-approach of literature review and expert opinion to identify the factors of RSC. For the development of the structural model and clustering of the factors, this paper has used the total interpretive structural modeling approach with Matrice d’Impacts Croises-Multiplication Applique and Classment and decision-making trial and evaluation laboratory analysis.FindingsIn total, this study has identified 17 factors that enable the 3R capability of the RSC. The result shows that the factors have a close dependence relationship with supply chain (SC) risk management culture as the most influencing factor. Further, this study classifies the factors into enablers and strategies.Research limitations/implicationsThis research work is the theoretical contribution to the RSC concept and helps the experts to develop and improve the resilient ability in the SC. This research is based entirely on subjective expert feedback; thus, the results are sensitive to the expert’s judgment.Practical implicationsThis research will help the decision-makers in allocating the resources and policies to develop or improve the SC capabilities.Originality/valueThis research work is the first kind of research in the field of the RSC that considers the 3R concept to identify and model the resilient factors of the SC. Along with the theoretical concept, this research provides empirical evidence for the importance ranking of the factors.

PurposeCategorizing projects allows for better alignment of a portfolio with the organizational strategy and goals. An appropriate project categorization helps understand portfolio’s structure and enables proper project portfolio selection (PPS). In practice, project categorization is, however, conducted in intuitive approaches. Furthermore, little attention has been given to project categorization methods in the project management literature. The purpose of this paper is to provide researchers and practitioners with a data-driven project categorization process designed for PPS.Design/methodology/approachThe suggested process was modeled considering the main characteristics of project categorization systems revealed from the literature. The clustering analysis is used as the core-computing technology, allowing for an empirically based categorization. This study also presents a real-world case study in the automotive industry to illustrate the proposed approach.FindingsThis study confirmed the potential of clustering analysis for a consistent project categorization. The most important attributes that influenced the project grouping have been identified including strategic and intrinsic features. The proposed approach helps increase the visibility of the portfolio’s structure and the comparability of its components.Originality/valueThere is a lack of research regarding project categorization methods, particularly for the purpose of PPS. A novel data-driven process is proposed to help mitigate the issues raised by prior researchers including the inconsistencies, ambiguities and multiple interpretations related to the taken-for-granted categories. The suggested approach is also expected to facilitate projects evaluation and prioritization within appropriate categories and contribute in PPS effectiveness.

PurposeThis study aims to design a supply chain network in an uncertain environment while exists two options for distribution of the perishable product and production lot-sizing is concerned.Design/methodology/approachOwing to the complexity of the mathematical model, a solution approach based on a Lagrangian relaxation (LR) heuristic is developed which provides good-quality upper and lower bounds.FindingsThe model output is discussed through various examples. The introduction of some enhancements and using some heuristics results in better outputs in the solution procedure.Practical implicationsThis paper covers the modeling of some real-world problems in which demand is uncertain and managers face making some concurrent decisions related to supply chain management, transportation and logistics and inventory control issues. Furthermore, considering the perishability of product in modeling makes the problem more practically significant as these days there are many supply chains handling dairy and other fresh products.Originality/valueConsidering uncertainty, production, transshipment and perishable product in the inventory-routing problem makes a new variant that has not yet been studied. The proposed novel solution is based on the LR approach that is enhanced by some heuristics and some valid inequalities that make it different from the current version of the LR used by other studies.

PurposeThe purpose of this paper was exploring and comparing different deterioration models based on statistical and machine learning approaches. These models were chosen from their successful results in other case studies. The deterioration models were developing considering two scenarios: (i) only the age as covariate (Scenario 1); and (ii) the age together with other available sewer characteristics as covariates (Scenario 2). Both were evaluated to achieve two different management objectives related to the prediction of the critical condition of sewers: at the network and the sewer levels.Design/methodology/approachSix statistical and machine learning methods [logistic regression (LR), random forest (RF), multinomial logistic regression, ordinal logistic regression, linear discriminant analysis and support vector machine] were explored considering two kinds of predictor variables (independent variables in the model). The main propose of these models was predicting the structural condition at network and pipe level evaluated from deviation analysis and performance curve techniques. Further, the deterioration models were exploring for two case studies: the sewer systems of Bogota and Medellin. These case studies were considered because of both counts with their own assessment standards and low inspection rate.FindingsThe results indicate that LR models for both case studies show higher prediction capacity under Scenario 1 (considering only the age) for the management objective related to the network, such as annual budget plans; and RF shows the highest success percentage of sewers in critical condition (sewer level) considering Scenario 2 for both case studies.Practical implicationsThere is not a deterioration method whose predictions are adaptable for achieving different management objectives; it is important to explore different approaches to find which one could support a sewer asset management objective for a specific case study.Originality/valueThe originality of this paper consists of there is not a paper in which the prediction of several statistical and machine learning-based deterioration models has been compared for case studies with different local assessment standard. The above to find which is adaptable for each one and which model is adaptable for each management objective.

PurposeThis paper aims to propose an alternate, efficient and scalable modeling framework to simulate large-scale bike-sharing systems using discrete-event simulation. This study uses this model to evaluate several initial bike inventory policies inspired by the operation of the bike-sharing system in Mexico City, which is one of the largest around the world. The model captures the heterogeneous demand (in time and space) and this paper analyzes the trade-offs between the performance to take and return bikes. This study also includes a simulation-optimization algorithm to determine the initial inventory and present a method to deal with the bias caused by dynamic rebalancing on observed demand.Design/methodology/approachThis paper is based on the analysis of an alternate and efficient discrete-event simulation modeling framework. This framework captures the heterogeneity of demand and allows one to experiment with large-scale models. This study uses this model to test several initial bike inventory policies and also combined them with an optimization engine. The results, provide valuable insights not only for the particular system that motivated the study but also for the administrators of any bike-sharing system.FindingsThe findings of this paper include: most of the best policies use a ratio of bikes: docks near to 1:2; however, it is important the way they are initially allocated; a policy that contradicts the demand profile of the stations can lead to poor performance, regardless the quick and dynamic changes of bike locations during the morning period; the proposed simulation-optimization algorithm achieves the best results.Research limitations/implicationsThe findings are limited to the initial inventory of the system under study. The model assumes a homogeneous probability distribution function for the travel time. This assumption seems reasonable for the system under study. This paper limits the tested inventory policies to simple practical rules. There might be other sophisticated methods to obtain better solutions, but they might be system-specific.Practical implicationsThe insights of this paper are valuable for operators of bike-sharing systems because this study focuses on the analysis of the impact of the initial inventory assuming that dynamic rebalancing may not be existing during the morning peak-time. This paper finds that initial inventory has a great impact on the performance, regardless of how quickly the bikes are dispersed across the system. This study also provides insights into the effect of dynamic rebalancing on observed demand.Social implicationsIncreasing knowledge about the operation of the bike-sharing system has a positive effect on society because more cities around the world could consider implementing these systems as a public transportation mode. Furthermore, delivering suggestions on how to increase the user service level could incentivize people to adopt bikes as a mobility option, which would contribute to improve their health and also reduce air pollution caused by motorized vehicles.Originality/valueThis paper considers that the contributions of this work to existing literature are the following: this study proposes a novel efficient and scalable simulation framework to evaluate initial bike inventory policies; the analysis presented in the paper includes an approach to deal with the bias in the observed demand caused by dynamic rebalancing and the analysis includes the value of demand information to determine an effective initial bike inventory policy.

PurposeThe paper aims to analyze the relationships between ethical leadership (EL), job satisfaction (JS), affective commitment (AC) and turnover intention (TI) that might make accountants quit withdrawal and become productive and useful in private organizations operating in Istanbul.Design/methodology/approachData were collected through an online survey using a simple random sampling methodology, obtained from 153 accountants working in companies in Istanbul. The methodology included descriptive statistics, factor analysis, structural equation modeling and mediation analysis.FindingsConcerning direct relationships between EL, JS, AC and TI, EL has significant positive associations with JS and AC, whereas EL has a significant negative association with TI. JS has a significant positive association with AC, whereas JS has a significant negative association with TI. Also, AC has a weak significant negative association with TI. Given indirect relationships among EL, JS, AC and TI, JS and AC mediate the relationship between EL and TI. Finally, a similarity is found when comparing Generation X and Generation Y in terms of overall JS, AC and TI.Research limitations/implicationsThe study is limited solely to companies functioning in Istanbul and incorporates a low number of respondents. Therefore, the results cannot be considered to be accurate for the whole country. The study might guide both private and public organizations in which owners/managers develop strategic plans.Originality/valueThe study fills the gap in research on organizational behavior where little has existed until now that probes the EL–JS–AC–TI links in Turkey. A few studies measure the TIs of accountants. Furthermore, EL and AC are rarely evaluated in the field of accounting in Turkey.

PurposeAirlines use various analytical techniques to optimally control seats in its various fare classes. Most literature on inventory control does not discuss policies for group arrivals. This study aims to address the literature gap in determining an optimal booking policy which considers the dynamics of group behaviour with respect to cancellations and refunds.Design/methodology/approachAn analytical model by which bid price control mechanism can be applied to group bookings is developed through this study.FindingsNumerical experiments are performed to demonstrate the analytical model under various scenarios. A comparison of the model with the traditional model indicates an increase in the total revenue generated.Originality/valueThe novelty of this work is in modelling the dynamics of group behaviour in a network of flights, and it contributes to the literature on revenue management.