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The purpose of this paper is to enlarge the exposure of the Theil–Sen (TS) methodology to the academic, analyst and practitioner communities using an earnings forecast setting. The study includes an appendix that describes the TS model in very basic terms and SAS code to assist readers in the implementation of the TS model. The study also presents an alternative approach to deflating or scaling variables.

Archival in nature using a combination of regression analysis and binomial tests.

The binomial test results support the hypothesis that the forecasting performance of the naïve no-change model is at least equal to or better than the ordinary least squares (OLS) model when earnings volatility is low. However, the results do not support the same hypothesis for the TS model nor do the results support the hypothesis that the OLS and TS models will outperform the naïve no-change model when cash flow volatility is high. Nevertheless, the study makes notable contributions to the literature, as the results indicate that the performance of the naïve model is at least as good as the OLS and TS models across 18 of the 20 binomial tests. Moreover, the results indicate that the performance of the TS model is always superior to the OLS model.

The results are generalizable to US firms and may not extend to non-US firms.

The TS methodology is advantageous to OLS in that the results are robust to outlier observations, and there is no heteroscedasticity. Researchers will find this study to be useful given the use of a model (i.e. TS) which has to date received little attention, and the provision of the details for the mechanics of the model. A bonus for researchers is that the study includes SAS code for implementing the procedure.

Awareness of alternative forecast methodologies could lead to improved forecasting results in certain contexts. The study also helps the financial community in general, as improved forecasting abilities are important for all capital market participants as they improve market efficiency.

Although a healthy literature exists for examining out-of-sample forecasts for earnings, the literature lacks an answer for a simple question before pursuing additional analyses: Are the results any better than those from a naive no-change forecast? The current study emphasizes the idea that the naïve no-change forecast is the most elementary model possible, and the researcher must first establish the superiority of a more complex model before conducting further analyses.

The paper introduced the use of a hybrid system of neural networks support vector machines (NNSVMs) consisting of artificial neural networks (ANNs) and support vector machines (SVMs) to price single-family properties.

The mechanism of the hybrid system is such that its output is given by the SVMs which utilise the results of the ANNs as their input. The results are compared to other property pricing modelling techniques including the standalone ANNs, SVMs, geographically weighted regression (GWR), spatial error model (SEM), spatial lag model (SLM) and the ordinary least squares (OLS). The techniques were applied to a dataset of 3,225 properties sold during the period, January 2012 to May 2014 in Cape Town, South Africa.

The results demonstrate that the hybrid system performed better than ANNs, SVMs and the OLS. However, in comparison to the spatial models (GWR, SEM and SLM) the hybrid system performed abysmally under with SEM favoured as the best pricing technique.

The findings extend the debate in the body of knowledge that the results of the OLS can significantly be improved through the use of spatial models that correct bias estimates and vary prices across the different property locations. Additionally, utilising the result of the hybrid system is thus affected by the black-box nature of the ANNs and SVMs limiting its use to purposes of checks on estimates predicted by the regression-based models.

The main purpose of this paper is to construct a conceptual model that addresses one of the most urgent matters for contemporary organizations, which is how organizations are to learn and integrate sustainability in its’ working processes. The guiding research question reverberates around how organizational learning (OL) and corporate entrepreneurship (CE) can promote and facilitate sustainability practices in organizations.

This paper uses knowledge from OL and CE theories representing tools to think with for an exploration of how to actualize sustainability practices in organizations.

This paper construes and presents a four-phase multilevel conceptual model for the analysis and creation of sustainability practices using insights from OL and CE. OL contributes with vital parts for creating sustainability practices in organizations delineated by continuous feedback and feedforward loops on individual, group, organizational and societal levels. CE prompts essential process and concrete working elements accentuating the importance of acquiring sustainability in all phases of the change process.

The outlined conceptual model is general and need to address the deeper complexities and context dependencies of sustainable practice in organizations in a more elaborate form. Thus, the proposed model calls for empirical scrutiny and further theoretical development.

Derived from two interrelated fields of research, this paper contributes with a novel model addressing how sustainability practices can be conceptualized and facilitated in organizations. By using OL theory in combination with CE studies, the proposed model seeks to capture key elements to guide new understandings of and transitions to sustainable practices in organizations.

This study investigated the impact of the virtualization requirements of the learning process on students' satisfaction and their intention to continue using online learning.

A research model was developed using the process virtualization theory (PVT); it was validated empirically using data obtained from an online questionnaire-based survey of 489 undergraduate students.

The main results support the role of representation for sensory requirements, sensory requirements, reach, representation for relationship requirements and relationship requirements on shaping students' satisfaction, which all also have a significant influence on students' intention to continue using online learning. Relevance factors are responsible for 61.6% of the variance in students' satisfaction and 83.6% of the variance in their intention to continue using online learning. However, neither the synchronism requirements nor the identification and control requirements had a significant effect on students' satisfaction or on their intention to continue using online learning.

The present research focused on PVT in an online learning context; consequently, a new set of factors that influenced students' satisfaction with and intentions to continue using online learning was empirically tested for the first time. This research contributes to the literature on information systems because it advances the generalizability and applicability of the PVT in a new context and new cultural setting. Moreover, the research apprises researchers and practitioners of new factors, which should be understood and fulfilled to make virtual learning equivalent to the face-to-face learning experience.

The purpose of this paper is to propose a model for organizational learning (OL) that can help organizations to transform into a learning-driven organization (LDO); a model that considers the whole ecosystem, its subsystems and considers the importance of technology, digitalization and dataism. The authors seek to answer key questions, specifically, first, what makes an organization learning-driven? and, second, how the learning ecosystem works organization-wide?

This paper draws on prior research conducted by the authors in the hospitality sector. Insights were gleaned from both theoretical perspectives and qualitative data drawn from a number of empirical studies. This paper focuses on critically reviewing the literature on OL, and selected organizational development frameworks such as the European Foundation for quality management and investors in people.

The authors propose an ecosystem model that entails three subsystems for OL. At this stage, the authors propose a conceptual framework that will be tested in the following part two. Leaders in organizations need to re-design their organizations to incorporate learning at all levels, i.e. individuals, teams and organization-wide. Learning should be an overarching approach within and beyond the boundaries of the organizations; for organizations to learn effectively, learning should be strategized and institutionalized.

This paper sheds light on the emerging trends in OL in light of the Industry 4.0 revolution with its phenomenal impact on humans and workplace; there is a dire need for research on human-machine balance, role and impact of machine learning and AI technologies. The authors call for setting up an updated agenda for learning and reconstructing learning into the corporate world; not only this but the future research should focus on reviewing and evaluating what did the authors learn about learning and how can the authors further learn, unlearn and re-learn.

The authors argue that organizations should look into learning as an enabler toward creativity and innovation, which should ultimately lead to excellence and fulfilling the needs of all stakeholders. Organizations should be consciously aware of their emerging intangible assists and proactively encourage their people toward more creativity. Learning can be institutionalized, and the organization transforms into a LDO.

The authors propose an ecosystem model that entails three subsystems for OL. At this stage, the authors propose a conceptual framework that will be tested in the following part two. Leaders in organizations need to re-design their organizations to incorporate learning at all levels, i.e. individuals, teams and organization-wide. Learning should be an overarching approach within and beyond the boundaries of the organizations; for organizations to learn effectively, learning should be strategized and institutionalized.

The LDO model will help organizations to strategize learning. Strategic learning about understanding a global strategy and how each business unit in an organization contributes its best, most innovative thinking followed by actions that execute the strategic intent of the organization.

The purpose of this study is to investigate the relationship between organizational learning (OL) and business sustainability (BS) and to carry out its empirical examination.

Extensive literature research was carried out. Then, an empirical study was conducted in 694 Polish and Danish companies. Two phenomena related to OL were adopted: OL processes and organizational learning capability (OLC). BS was examined through the concept of sustainable performance (SP). Research models were tested using structural equation modeling.

The empirical studies have shown a positive, statistically significant relationship between OL and BS. The research supports the view that the intensification of the OL processes is substantial for BS, whereas the OLC concept is less relevant to the development of BS. The effect of OL on total SP was stronger than on any SP dimension. OL supports the synergies of the results obtained by the organization for the benefit of BS.

The model verification is based on the samples from two countries, and, therefore, the hypothesis requires further verification in different business contexts. In addition, there are different factors influencing BS, which have not been included in the research and should be analyzed in the future.

An in-depth, critical literature analysis shows that the theoretical foundation of the role of OL in shaping BS is fragmented and poorly empirically verified. The value of this paper is the presentation of large-scale empirical studies comparing the relationship between BS and two phenomena: OLC and the OL processes. The results obtained in the course of the research open up new research directions with respect to both the relationship between OL and BS as well as between OL and organizational performance.

The purpose of this paper is to estimate a three‐equation model of US commercial bank usage of the Farm Service Agency's (FSA) guaranteed operating loan and interest assistance programs. Also, to identify the key farm and banking variables that affect the decision to use loan guarantees and the volume of loans with interest assistance.

A triple hurdle, three‐equation system is estimated to model three decisions: to participate in the FSA operating loan program; whether to use interest assistance given the decision to participate in the operating loan program; and then the degree of participation in the interest assistance program. Statistical selection is modeled. Data on almost all commercial banks in the USA from 1995 to 2003 are used in the estimation sample.

Statistical selection is statistically significant so selection must be included in the models. Variables reflecting state‐level characteristics such as farm debt servicing ratio, individual bank loan‐to‐asset ratio, bank size and the general guaranteed loan and interest assistance environment are significant in all three equations. Intensity of interest assistance use varies markedly across states.

The interest assistance program has high subsidy costs and is an important source of support for financially marginal farmers. Scant prior research has investigated this program. The present study also shows that modeling interest assistance usage must be embedded in a larger model to give a complete specification.

This paper aims to analyze the influence of organizational learning (OL) and service innovation (SI) on organizational performance of knowledge-intensive business services (KIBS) and examine the mediating role of SI.

Hypotheses were tested using the theoretical OL model of knowledge acquisition, distribution, interpretation and organizational memory (Huber, 1991; Lopez, Peon, & Ordas, 2005; Jiménez-Jiménez & Sanz-Valle, 2011), using structural equation modeling partial least squares analysis of a survey data set of Brazilian architectural firms.

Findings suggest that OL is significantly linked to SI and so is SI to organizational performance. However, neither the direct relationship between OL and organizational performance could be verified, nor the mediating effect of SI.

These results can offer KIBS managers insights that suggest that OL alone does not guarantee a significant impact in organizational performance, but it is a starting point for achieving SIs, that lead to performance improvement and competitive advantages.

This paper contributes to the knowledge production in the following ways: to the understanding of the relationship between OL and SI and its effect on organizational performance, traditionally overlooked in the literature; to the study of SIs, considering the importance of the service sector; and to the study of innovation processes in architectural firms, a sector traditionally understudied, because of the focus on large construction firms.

We searched JM from 1990 to 2005 to identify all studies that employed OLS regression.¹ The search resulted in 83 articles with 147 OLS regressions. Many authors specifically state that they used OLS and these were promptly included in the sample. Other authors acknowledged regression as a methodological procedure without explicitly specifying an estimation technique. To inquire whether OLS was used, we communicated with all of these authors. In all, 51 authors were contacted, with 44 responding. Of the 44 that responded 43 had used OLS; the only exception was subsequently excluded from the analysis. The remaining seven articles were checked once more, and consensus was reached that OLS had most likely been used. Therefore, they were also included in the study.

Structuring learning and maximising the use of knowledge in manufacturing organisations can further Trinidad and Tobago’s (T&T) quest to diversify its energy‐based economy, promote sustainable development and enhance the creativity and competence of its population. Existing Organisational Learning (OL) and Knowledge Management (KM) models have not sufficiently integrated soft elements (e.g., culture) and hard elements (e.g., technology) to enable direct application within T&T’s manufacturing sector. This paper discusses the conceptual foundations of OL/KM, and identifies several key OL/KM elements (such as culture, KM tools and instruments, learning processes and learning practices) that would be used to devise a holistic manufacturing OL model. A research agenda is also presented, by which the model would be validated.