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It is possible to see effective use of Artificial Intelligence-based systems in many fields because it easily outperforms traditional solutions or provides solutions for the problems not previously solved. Prediction applications are a widely used mechanism in research because they allow for forecasting of future states. Logical inference mechanisms in the field of Artificial Intelligence allow for faster and more accurate and powerful computation. Machine Learning, which is a sub-field of Artificial Intelligence, has been used as a tool for creating effective solutions for prediction problems.

In this chapter the authors will focus on employing Machine Learning techniques for predicting data for future states of economic using techniques which include Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, Dynamic Boltzmann Machine, Support Vector Machine, Hidden Markov Model, Bayesian Learning on Gaussian process model, Autoregressive Integrated Moving Average, Autoregressive Model (Poggi, Muselli, Notton, Cristofari, & Louche, 2003), and K-Nearest Neighbor Algorithm. Findings revealed positive results in terms of predicting economic data.

Organizational studies fail to examine organizations in terms of the several environments in which they operate, both internally and externally. That is, studies tend to focus on climate, or time, or trust, or leadership. This chapter builds on academic research that discusses organizational environments in ways that show all of these environments are important for organizational understanding, especially for organizational leadership. In particular, this chapter offers a paradigm of understanding organizational leadership realities through multi-level understanding of the organizational environments of climate, knowledge, ethnos, and time.

The chapter first discusses five enviroscapes – climate, knowledge, ethos, time, and leadership. Each of these enviroscapes has two phenotypes – business and commerce. Each of these enviroscapes, with its concomitant phenotypes, is used differently at multiple levels of management and leadership by senior managers, middle managers, and entry-level managers. The scope of organizational reach, in terms of global, regional, and local levels of analysis, provides additional context for the use of enviroscapes. After a review of the theoretical bases for each enviroscape, the chapter applies appropriate theory and models to an extended time case study of land purchase in Indonesia.

The common methods for clustering time series are the use of specific distance criteria or the use of standard clustering algorithms. Ensemble clustering is one of the common techniques used in data mining to increase the accuracy of clustering. In this study, based on segmentation, selecting the best segments, and using ensemble clustering for selected segments, a multistep approach has been developed for the whole clustering of time series data.

First, this approach divides the time series dataset into equal segments. In the next step, using one or more internal clustering criteria, the best segments are selected, and then the selected segments are combined for final clustering. By using a loop and how to select the best segments for the final clustering (using one criterion or several criteria simultaneously), two algorithms have been developed in different settings. A logarithmic relationship limits the number of segments created in the loop.

According to Rand's external criteria and statistical tests, at first, the best setting of the two developed algorithms has been selected. Then this setting has been compared to different algorithms in the literature on clustering accuracy and execution time. The obtained results indicate more accuracy and less execution time for the proposed approach.

This paper proposed a fast and accurate approach for time series clustering in three main steps. This is the first work that uses a combination of segmentation and ensemble clustering. More accuracy and less execution time are the remarkable achievements of this study.

The study aims to examine the patterns of time allocated to paid employment activities by women in India as well as change in time allocating pattern of women over the period 1998 to 2019. In doing so, it attempts to highlight gender-asymmetry of time use and heterogeneity in time use of women residing in urban and rural areas as well as variations in time use by marital status, motherhood and age.

Using unit-level data from two available Time Use Surveys (TUS) namely the Pilot Survey TUS 1998 and first nationally representative TUS 2019, the authors use Tobit model to estimate determinants of women’s time in employment. To explain the change in time spent on paid work by women over the two decades, the authors use counterfactual quantile regression decomposition.

The gender asymmetry in time allocation is stark, with women spending one fifth time compared to men paid employment activities. Over the two decades of interest, women’s time spent on employment activities in a day has reduced by half from around 4 h to 2 h, largely driven by rural women’s time. Regression results suggest the emergence of a “U-shaped” relationship between time spent on paid work and education of women. The counterfactual decomposition results suggest that women are spending lesser time on employment activities in 2019 than in 1998 across the time distribution.

This paper contributes to a novel understanding of time use by women in a developing country by analysing the changes in time use over two decades as well as distributional sensitivity to observed characteristics. The study informs about the intensive margins of female employment by incorporating dynamics of socio-economic development.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2022-0164.

DURING some comments on the brain drain last month it was remarked that work study technologists stood on the periphery. Suddenly they have been moved right to the centre as the result of a communication from Dr. Robert N. Lehrer. He is among the six American work study experts best known to the profession in this country, ranking with Barnes and Mundel as having contributed much to a right appreciation of the subject's value and its techniques.

The broad aim of this paper is to investigate whether managers in Australia allocate their time differently than other occupational groups, and the impact gender and life situation (using marital status and presence or absence of dependent children as a proxy) has on time allocation.

To address these broad aims, data are drawn from the 1997 Australian Time Use Survey. This is a nationally representative survey that examines how people in different circumstances allocate time to different activities.

The results of this study highlight three important issues. The first is that male and female managers display different patterns of time use. Male managers' time is dominated by paid employment activities, whereas female managers' time is spent predominantly on employment and domestic activities. The second is that life situation impacts on the time use of female managers, but not male managers. The third important find of this study is that managers' time use is different to other occupational groups.

These findings have policy implications relating to work‐life balance, career progression and changes in patterns of work. In terms of work‐life issues, it reveals that male and female managers face a “time squeeze”, with some evidence of a “second‐shift” for female managers. In addition, the findings provide insight into the work‐life issues faced by male and female managers.

The results of this inquiry provide insight into how different individuals spend their time – insight into “lifestyles”. However, in‐depth qualitative studies are required to reveal why individuals allocate their time in this way and to understand the opportunities and constraints individuals face in time allocation.

Microfinance impact evaluation studies help in discovering client needs which are diverse, special and different from the needs of the conventional bankable clients. Thus, such area of market research is becoming essential for microfinance institutions for designing better client-centred products. In this research, the authors discuss the specific model of household economic portfolio (HEP) for qualitative impact evaluation in microfinance. The paper aims to discuss the complexity limitations of the HEP. Solutions are provided for overcoming these limitations. The modified household economic portfolio (M-HEP) model is simplified and detailed, and two types of diaries are suggested for implementing it.

First, the authors briefly review the literature on impact assessment methods in microfinance and on the HEP model. In the second part of the paper, the M-HEP is suggested and discussed in detail. In the third part, the authors present a case study to illustrate the additional information that can be generated by using our suggested research tool and model. Finally, the authors wrap up with a summary of the findings.

Solutions are provided for overcoming the limitations of the HEP model. The suggested model (M-HEP) is simplified and detailed, and two types of diaries are suggested for implementing it. The case study shows that, certainly, time and money are related. While time may mean money for a rich person, for a poor person, if money is not forthcoming, she may spend time on non-income generating work that adds to her social esteem. She may also consume inexpensive assets because spending time at low cost is important. Finally, she spends time in conducting activities for which she cannot afford to pay.

The paper offers two novelties. First, it details the interactions between the elements of the HEP model of Chen and Dunn. This improvement to the original model is highly important for defining the measures that are required for redrawing the economic portfolio of an individual. The second novelty is in suggesting the collection of time-use and financial daily data. To the best of our knowledge, this is the first time a combined diary is used in microfinance research. These two novelties allow the application of a modified version of the highly interesting HEP model in spite of its complexity.

Introduction Operations research, i.e. the application of scientific methodology to operational problems in the search for improved understanding and control, can be said to have started with the application of mathematical tools to military problems of supply bombing and strategy, during the Second World War. Post‐war these tools were applied to business problems, particularly production scheduling, inventory control and physical distribution because of the acute shortages of goods and the numerical aspects of these problems.