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This paper aims to explore the impact of time on customer behavior and decision making. Even though people factor time into decisions on a daily basis, businesses tend to focus resources and efforts on other aspects of consumer behavior such as pricing and demographics, resulting in many executives overlooking this key element of the customer decision process.

This paper provides a framework for understanding how customers value offerings in relation to time and attention priorities. It also demonstrates the evolution of today's time scarcity in relation to commerce and why it is more salient today using time‐use data.

Companies that understand how customers value time in relation to their offerings were more successful in today's economy and often achieved competitive advantage.

The framework guides executives in determining actions and market opportunities for products or services based on a time and attention‐centric mindset.

Time‐value economics provides a new lens for organizations to understand how time shapes customer behavior and decisions. The associated framework provides a useful market segmentation model. It enables businesses to identify new innovation opportunities and actions to foster growth.

The purpose of this article is to provide an interview with Dr Stanley C. Abraham.

The paper provides an interview with Stanley C. Abraham, author of Strategic Planning.

In the interview, Dr Abraham discusses his background in strategic planning and consultancy, and his role as co‐founder of the Association for Strategic Planning.

The interview highlights the benefits of Dr Abraham's “Strategic Analysis Model (That Works).”

Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.

This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.

The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.

Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.

The purpose of this paper is to evaluate competing explanations for the greater prevalence of informal employment in some countries rather than others. These variously explain informal employment to be a result of either economic under-development and the lack of modernisation of governance (“modernisation” theory), higher taxes and too much state intervention (“neo-liberal” theory) or inadequate government intervention to protect workers from poverty (“political economy” theory).

To do this, an International Labour Organisation data base produced in 2018 on the prevalence of informal employment in 112 countries (comprising 90 per cent of the global workforce) is analysed, and macro-level economic and social conditions reflecting each of these theories tested using bivariate regressions.

The prevalence of informal employment ranges from 94.6 per cent of total employment in Burkina Faso to 1.2 per cent in Luxembourg. Evaluating the validity of the competing theories, neo-liberal theory is refuted, and a call made to synthesise the modernisation and political economy perspectives in a new “neo-modernisation” theory that tentatively associates the greater prevalence of informal employment with lower economic under-development, greater levels of public sector corruption, smaller government and lower levels of state intervention to protect workers from poverty.

This paper tentatively reveals the structural economic and social conditions that need to be addressed globally to reduce informal employment.

This is the first paper to report the results of a harmonised data set based on common criteria to measure the varying prevalence of informal employment globally (across 112 countries representing 90 per cent of global employment) in order to determine the structural economic and social conditions associated with higher levels of informal employment.