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Personal characteristics, such as age, marital status, education level and gender, vary among elderly residents in residential apartments. These characteristics may influence the elderly residents’ satisfaction with the facilities provided in their residential apartments. To ensure appropriate facilities management (FM) items are provided for the different categories of elderly people, it is necessary to understand their basic needs. Therefore, this paper aims to compare the satisfaction with FM items among elderly people with different personal characteristics in private domestic (PD) buildings.

A questionnaire survey was conducted among elderly people with different personal characteristics in PD buildings to collect information about their levels of satisfaction with FM items. A total of 41 FM items and four characteristics of the elderly, namely, age, gender, marital status and education, were identified in this study.

The result shows that satisfaction with natural daylight was significantly different among elderly people of different genders, while the one-way between-groups ANOVA indicate that satisfaction with the size of bedrooms, turning spaces at doors, temperature in bathrooms and/or toilets, colour, accessibility and ease of closing or opening the doors were significantly different among elderly people belonging to different age groups and of different marital status and education level.

Designers and private developers are therefore recommended to increase the sizes of bedrooms, install windows on opposite sides of walls in the flats and ensure there is an adequate light reflection ratio for wall and floor colours, to accommodate elderly people’s special characteristics.

This paper seeks to explore the applicability and implications of Bourdieu's field‐capital theory for marketing using original research with a typical European society. Bourdieu's field‐capital theory proposes that people acquire economic, social and cultural capital which they deploy in social arenas known as “fields” in order to compete for positions of distinction and status. This exploratory study aims to examine how Bourdieu's theory may explain competitive behavior in fields of interest to marketers.

A total of 61 in‐depth interviews were completed with respondents that were representative of each of 61 geodemographic “types” – clusters that enable marketers to segment an entire population.

The findings suggest that examining human behaviour through the lens of field and capital theory highlights the importance of the competition motive in explaining consumers' behaviour. New “fields” were identified which seem to have assumed primary importance, particularly in middle‐class people's lives.

Viewing consumer behaviour as social competition implies that new segmentation approaches may yield successful marketing outcomes, and opens consumer psychology and behaviour itself to new interpretations.

Very few research papers that apply field‐capital theory to marketing are present in the literature. It is hoped that this work addresses an important area, and one that is particularly prevalent in twenty‐first century consumerism.

The purpose of this paper is to measure technical efficiencies, slacks and input/output targets for 50 large Indian pharmaceutical firms.

The data are collected from Prowess of Centre for Monitoring of Indian Economy for the financial year 2010-2011. This study uses data envelopment analysis approach, taking raw material, salaries and wages, advertisement and marketing and capital usage cost as input variables and net sales revenue as output variable.

The paper finds that out of 50 firms, nine firms were overall technical efficient while 19 firms pure technical efficient and thus defined the efficient frontier. The BCC model identified that the inefficiency is either due to inefficient managerial performance or scale utilization. Further, firms are classified as high, low and middle robust firms on the basis of peer count. The study also analysed the slacks which were found to be significant in regard of some inputs, especially advertisement and marketing. The targets setting results have shown that all the inputs have significant scope for reduction.

The empirical results are useful in assessing the relative efficiency of the large Indian drug and pharmaceutical industry (ID&P) firms. The managers and owners can take corrective actions to reduce the cost of operations by optimizing advertising and marketing cost, capital usage cost and salary and wages so as to improve their efficiency.

Unlike the previous studies on the efficiency of the ID&P industry, the paper have shown the significance of improvement in managerial performance and scale utilization. In addition to this, excess inputs used in the production process and also possible target values of inputs and outputs are shown in the study. The robustness and stability of efficiency scores is also checked.

This paper makes a novel attempt to estimate the potential impact of credit risk on foreign direct investment (FDI hereafter), thereby focusing on a completely unexplored area in the existing empirical literature.

To provide a comprehensive understanding of the relationship between credit risk and FDI inflows, the study incorporates all the eight-member economies of the South Asian Association of Regional Cooperation (SAARC hereafter) and analyzes a panel data set, over the period 2011 to 2019, extracted from the World Development Indicators, using the suitable econometric techniques for the efficient estimations of the specified models.

The results indicate a negative and statistically significant relationship between the credit risk of the banking sectors and FDI inflows. Similarly, market size and inflation rate appear to be the two other main factors behind the increasing FDI inflows in the SAARC member economies. Interestingly, the size of the market became irrelevant in attracting FDI inflows when the Indian economy is excluded from the sample due to its higher economic weight. On the other hand, FDI inflows are not dependent on the level of trade openness, with most of the specifications showing either an insignificant or negative coefficient of the variable.

The obtained results are unique and robust to alternative methodologies, and hence, the SAARC economies could consider them as the critical inputs in formulating the appropriate policies on FDI inflows.

The findings are unique and original. The authors have established a relationship between credit risk and FDI for the first time in the SAARC context.

The accurate prediction of incoming cash flows enables more effective cash management and allows firms to shape firms' planning based on forward-looking information. Although most firms are aware of the benefits of these forecasts, many still have difficulties identifying and implementing an appropriate prediction model. With the rise of machine learning algorithms, numerous new forecasting techniques have emerged. These new forecasting techniques are theoretically applicable for predicting customer payment behavior but have not yet been adequately investigated. This study aims to close this research gap by examining which machine learning algorithm is the most appropriate for predicting customer payment dates.

By using various machine learning algorithms, the authors evaluate whether customer payment behavior patterns can be identified and predicted. The study is based on real-world transaction data from a DAX-40 firm with over 1,000,000 invoices in the dataset, with the data covering the period 2017–2019.

The authors' results show that neural networks in particular are suitable for predicting customers' payment dates. Furthermore, the authors demonstrate that contextual and logical prediction models can provide more accurate forecasts than conventional baseline models, such as linear and multivariate regression.

Future cash flow forecasting studies should incorporate naïve prediction models, as the authors demonstrate that these models can compete with conventional baseline models used in existing machine learning research. However, the authors expect that with more in-depth information about the customer (creditworthiness, accounting structure) the results can be even further improved.

The knowledge of customers' future payment dates enables firms to change their perspective and move from reactive to proactive cash management. This shift leads to a more targeted dunning process.

To the best of the authors' knowledge, no study has yet been conducted that interprets the prediction of incoming payments as a daily rolling forecast by comparing naïve forecasts with forecasts based on machine learning and deep learning models.

The purpose of this paper is to examine the impact of real exchange rate misalignment on economy and economic sectors, namely construction, manufacturing and mining and quarrying in Malaysia.

The equilibrium real exchange rate and economic models are estimated using the autoregressive distributed lag approach.

An increase in productivity differential or reserve differential will lead to an appreciation of real exchange rate in the long run. An increase in positive (negative) real exchange rate misalignment will lead to an increase (decrease) in economy. An increase in long-run real exchange rate misalignment will lead to a decrease in economy. Real exchange rate misalignment or long-run real exchange rate misalignment can influence the manufacturing sector in Malaysia. More specifically, undervaluation will promote whereas overvaluation will hurt the manufacturing sector.

Real exchange rate misalignment can be a policy to influence economy but may not be the best choice.