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To determine how to best deploy the Triple-A supply chain (SC) capabilities (AAA-agility, adaptability and alignment) to improve competitive advantage (CA) by identifying the Triple-A SC model with the highest CA predictive capability.

Assessment of in-sample and out-of-sample predictive capacity of Triple-A-CA models (considering AAA as individual constructs) to find which has the highest CA predictive capacity. BIC, BIC-Akaike weights and PLSpredict are used in a multi-country, multi-informant, multi-sector 304 plant sample.

Greater direct relationship model (DRM) in-sample and out-of-sample CA predictive capacity suggests DRM's greater likelihood of achieving a higher CA predictive capacity than mediated relationship model (MRM). So, DRM can be considered a benchmark for research/practice and the Triple-A SC capabilities as independent levers of performance/CA.

DRM emerges as a reference for analysing how to trigger the three Triple-A SC levers for better performance/CA predictive capacity. Therefore, MRM proposals should be compared to DRM to determine whether their performance is significantly better considering the study's aim.

Results with our sample justify how managers can suitably deploy the Triple-A SC capabilities to improve CA by implementing AAA as independent levers. Single capability deployment does not require levels to be reached in others.

First research considering Triple-A SC capability deployment to better improve performance/CA focusing on model's predictive capability (essential for decision-making), further highlighting the lack of theory and contrasted models for Lee's Triple-A framework.

The purpose of this paper is to consider the economic information content within several popular stock market factors and to the extent to which their movements are both explained by economic variables and can explain future output growth.

Using US stock portfolios from 1964 to 2019, the authors undertake three related exercises: whether a set of common factors contain independent predictive ability for stock returns, what economic and market variables explain movements in the factors and whether stock market factors have predictive power for future output growth.

The results show that several of the considered factors do not contain independent information for stock returns. Further, most of these factors are neither explained by economic conditions nor they provide any predictive power for future output growth. Thus, they appear to contain very little economic content. However, the results suggest that the impact of these factors is more prominent with higher macroeconomic risk (contractionary regime).

The stock market factors are more likely to reflect existing market conditions and exhibit a weaker relation with economic conditions and do not act as a window on future behavior.

Fama and French three-factor model still have better explanations for stock returns and economic information more than any other models.

This paper contributes to the literature by examining whether a selection of factors provides unique information when modelling stock returns data. It also investigates what variables can predict movements in the stock market factors. Third, it examines whether the factors exhibit a link with subsequent economic output. This should establish whether the stock market factors contain useful information for stock returns and the macroeconomy or whether the significance of the factor is a result of chance. The results in this paper should advance our understanding of asset price movement and the links between the macroeconomy and financial markets and, thus, be of interest to academics, investors and policy-makers.

The paper aims to investigate if the performance of older adults on the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) were associated or predictive of their functional performance in a geriatric evaluation and management (GEM) inpatient hospital setting. This will inform the occupational therapy assessment and management of older adults admitted to sub-acute GEM settings.

In all, 20 participants (11 men, 9 women, mean age 82 years, SD = 6.93) were recruited from a GEM ward in an Australian hospital. Participants’ cognitive abilities were assessed using the MMSE and MoCA, and their functional performance were assessed using the Functional Independence Measure (FIM). Spearman’s rho correlations and linear regression analyses were completed. Bootstrapping was applied to the regression analyses to accommodate the small study sample size.

No statistically significant correlations were obtained between the total and subscale scores of the MMSE and FIM or between the total and subscale scores of the MoCA and FIM. In other words, the cognitive and functional abilities of older adults admitted to a GEM setting were not significantly associated in this study.

The findings suggest that the MoCA and the MMSE were not predictive of participants’ functional performance as measure by the FIM in a sub-acute GEM setting. Occupational therapists should be cautious when interpreting participants’ MMSE, MoCA and FIM results and not depend solely on these results in the goal setting and intervention planning processes for clients on GEM wards. Further studies are recommended to confirm these findings.

The aim of this research is to analyze the success of digital transformation (DT) in the management and performance of organizations. To do so, the role of IT and its ability to integrate in organizations that provide professional services with high added value for their clients are investigated. These services require highly developed skills as they solve complex problems for the clients and this means that success depends on gathering knowledge from different sources (customers, public administrations and competitors). This study analyses the decisive and complementary role of IT in this process.

The analysis combines quantitative and qualitative methods. After questioning managers of Spanish KIBS companies about certain components of DT, the gathered data are subsequently processed with PLS-SEM to establish causal relationships.

The results show that digital capability is the determinant of DT. It has a positive effect on the digital resources integrated in KIBS companies and on their organizational performances.

Future research should continue to analyze other components of TD that drive the organizational performance of KIBS firms, such as technological culture or government policies that encourage digital transactions. The present study analyzes data from companies that are part of a single economic sector in Spain which may limit the conclusions drawn. It would be particularly useful to confirm the applicability of the results in companies operating in different markets to explore the direct relationship between digital capability and organizational performance.

This research has implications for managers of KIBS companies, as it shows the high potential of the ability of IT to implement and manage a TD process. Managers can benefit from IT management practices using the appropriate tools (ERP, CRM and management software) to gain more knowledge of customer behavior with the possibility of easily codifying and analyzing the data, which significantly influences innovation activities. The objective is to develop a strong internal capability to absorb knowledge from day-to-day interactions with customers by using IT effectively. This process leads to an improvement in the organizational performance of KIBS companies, as they become more effective in decision making with improved internal communication, generate greater employee satisfaction and reach new customers. Following strategies aimed at the implementation and use of the technological resources studied creates more agile firms and helps to close the production gap between SMEs and large companies.

The results obtained can help create sustainable businesses through cloud-based technology tools. It can provide insights for policy makers to implement economic policies that help SMEs to become more competitive and sustainable.

The development of digital technologies and the ability to manage them is one of the decisive factors that conceptualizes DT and improves organizational performance. This research contributes to the understanding of the need for managers of KIBS companies to follow strategies oriented towards the digitization of their organizations and for the collaborators to have a high level of IT training, especially in the use of cloud technology.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

The primary objective of this study is to investigate whether the inclusion of convertible bond prices as important inputs into artificial neural networks can lead to improved accuracy in predicting Chinese stock prices. This novel approach aims to uncover the latent potential inherent in convertible bond dynamics, ultimately resulting in enhanced precision when forecasting stock prices.

The authors employed two machine learning models, namely the backpropagation neural network (BPNN) model and the extreme learning machine neural networks (ELMNN) model, on empirical Chinese financial time series data.

The results showed that the convertible bond price had a strong predictive power for low-market-value stocks but not for high-market-value stocks. The BPNN algorithm performed better than the ELMNN algorithm in predicting stock prices using the convertible bond price as an input indicator for low-market-value stocks. In contrast, ELMNN showed a significant decrease in prediction accuracy when the convertible bond price was added.

This study represents the initial endeavor to integrate convertible bond data into both the BPNN model and the ELMNN model for the purpose of predicting Chinese stock prices.

This paper estimates a Nelson-Siegel model under the state-space representation in order to circumvent the shortcomings of the conventional Nelson-Siegel model and evaluates the predictive ability of the estimated model. The results indicate that the estimated Nelson-Siegel time-varying three factors have close relations to their counterparts : level, slope and curvature and the inflection of the Korean yield curve is located around the maturity of 55-month. Meanwhile, each factor is found to have unit-root but differenced-factors do not show signs of unit-roots, hence proved I (1) series. In order to assess the efficacy of the estimated model, we compare the yield prediction from our model with several natural competitors : random walk, Fama-Bliss, and Cochrane-Piazzesi. With respect to out-of-sample performance, Fama-Bliss model proves to be the worst in term structure forecasts in Korea. The predictive performance differs between the random walk and the state-space Nelson-Siegel model depending on the forecast horizon lengths. At the shorter horizon, the state-space Nelson-Siegel model outperforms the random walk, but the table is turned in the longer horizon

This paper aims to assess the application of seven statistical and data mining techniques to second-stage data envelopment analysis (DEA) for bank performance.

Different statistical and data mining techniques are used to second-stage DEA for bank performance as a part of an attempt to produce a powerful model for bank performance with effective predictive ability. The projected data mining tools are classification and regression trees (CART), conditional inference trees (CIT), random forest based on CART and CIT, bagging, artificial neural networks and their statistical counterpart, logistic regression.

The results showed that random forests and bagging outperform other methods in terms of predictive power.

This is the first study to assess the impact of environmental factors on banking performance in Middle East and North Africa countries.

Fashion clothing has always been an interesting area for scholarly research on consumer behavior. This paper seeks to gain a better understanding of the youth involvement with fashion clothing in the Egyptian context. Accordingly, the paper considers the Egyptian consumers’ attitude toward fashion involvement by investigating how fashion consciousness and materialism serve as main antecedents of fashion clothing involvement, while also determining the impact of fashion clothing involvement on fashion clothing purchase involvement. This paper aims to test an extended and adapted theoretical model of fashion clothing involvement in Egypt.

Using non-probabilistic convenience sample, a survey method was used, and 270 valid questionnaires were collected.

The hypothesized antecedents were found to influence fashion clothing involvement among young Egyptian consumers, which in turn significantly affect its purchasing. Moreover, materialism was also found to partially meditate the relationship between fashion consciousness and fashion involvement. On the other hand, the hypothesized gender role as a moderator between all variables of the study was not supported.

Using a wider population is one avenue future research seeking to replicate this study can pursue. Specifically, because the sample consisted of university students, generalizing the results to non-students can be restricted. Likewise, findings are mainly related to fashion clothing; hence, extending the model to include other product categories can provide more support for the results.

As the results confirmed that there is a partial significant positive impact of fashion consciousness on fashion clothing involvement via materialism, the paper provides practical implications for fashion marketers to achieve successful communication with fashion-conscious and materialistic young Egyptian consumers. The aim is to develop strategies that are consistent with consumers’ values and communicate appeals to their aspirational lifestyle.

This paper contributes to the limited number of the published manuscripts on the fashion clothing marketing sector in Egypt. There is a void in literature related the investigation of fashion clothing involvement in the developing countries. Accordingly, this paper fills this gap by examining the fashion clothing consumption behavior of young Egyptian students in Cairo University. To the best of the author’s knowledge, it is among the first to investigate the antecedents and motives related to fashion clothing involvement and its purchases among young consumers in the Egyptian context. The paper develops a comprehensive model of fashion clothing involvement to highlight the relationships between fashion involvement and Fashion consciousness, materialism, and fashion clothing purchase-involvement. The paper also contributes to the research by exploring materialism as a mediator between fashion consciousness and fashion involvement constructs, in addition to exploring the gender role as a moderator between all constructs of the study. The study makes theoretical contribution to the body of knowledge around young Egyptian consumers’ fashion clothing involvement and purchase behavior toward luxury fashion clothes, which may be extended to other similar Arab non-Western developing countries. Moreover, it offers managerial insights for establishing effective communications with this potentially lucrative market segment.

This paper aims to contribute to the debate over the desirability of introducing an accrual-based accounting system in the public sector by examining whether accrual-based accounting information is superior to cash-based information in the context of public sector entities.

This paper applies a quantitative research method to assess the degree of smoothness and relevance of the accrual components of income recorded by 302 entities of the Italian National Health Service (INHS) over the period 2014–2020.

The analysis reveals that net income is smoother than cash flows as a summary measure of economic results and that accounting for accruals improves the predictability of future cash flows. However, the authors' novel disaggregation of accrual accounts reveals that those accounts that contribute the most to making income smoother than cash flows – noncurrent assets and liabilities – are also those that contribute the least to predicting future cash flows.

The disaggregation of accrual accounts allows to identify the sources of the informational benefits of accrual accounting, and to document the existence of an informational “trade-off” between smoothness and relevance in the context of public sector entities.