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Measuring internal response of online learning is seen as fundamental to absorptive capacity which stimulates knowledge assimilation. However, the evaluation of practice and research of validated instruments that could effectively measure online learning response behavior is limited. Thus, in this study, a new instrument was designed based on literature to determine the structural variables that exist in the online learning response behavior.

A structured survey was designed and distributed to 410 Malaysian students enrolled in higher-education institutions. The questionnaire has 38 items, all of which were scored using a seven-point likert scale. To begin with, exploratory factor analysis with three types of extraction methods (i.e. principal component, principal axis factoring and maximum likelihood) was used as the method for comparing the outcomes of each extraction method's grouping variables by constantly using a varimax rotation method. In the second phase, reliability analysis was performed to determine the reliability level of the grouping variables, and finally, correlation analysis was performed to determine the discriminant nomological validity of the grouping variables.

The findings revealed that nine grouping variables were retrieved, with all items having a good value of factor loading and communalities, as well as an adequate degree of reliability. These extracted variables have good discriminant and nomological validity, as evidenced by correlation analysis, which confirmed that the directions of relationships among the extracted dimensions follow the expected theory (i.e. positive direction) and the correlation coefficient is less than 0.70.

This study proposes a comprehensive set of questionnaires that measure the student's online learning response behavior. These questionnaires have been developed on the basis of an extensive literature review and have undergone a rigorous process of validity and reliability for the purpose of enhancing students' online learning response behavior.

This study's findings will aid academic practitioners in assessing the online learning response behavior of students, as well as enhancing the questionnaire's boost factor when administered in an online learning environment.

In this paper, we discuss the application of the constitutive relation error (CRE) to model updating and validation in the context of uncertain measurements. First, a parallel is drawn between the CRE method and a general theory for inverse problems proposed by Tarantola. Then, an extension of the classical CRE method considering uncertain measurements is proposed. It is shown that the proposed mechanics‐based approach for model validation is very effective in filtering noise in the experimental data. The method is applied to an industrial structure, the SYLDA5, which is a satellite support for Ariane5. The results demonstrate the robustness of the method in actual industrial situations.

To investigate the feasibility of using artificial neural networks for conceptual design of bracings systems for tall steel buildings.

Database of 234 design examples has been developed using commercially available detailed design software. These examples represent building up to 20 storeys. Feed forward back‐propagation neural network is trained on these examples. The results obtained from the artificial neural network are evaluated by re‐substitution, hold‐out and ten‐fold cross‐validation techniques.

Results indicate that artificial neural network would give a performance of 97.91 percent (ten‐fold cross‐validation). The performance of this system is benchmarked by developing a binary logistic regression model from the same data. Performance of the two models has been compared using McNemar's test and receiver operation characteristics curves. Artificial neural network shows a better performance. The difference is found to be statically significant.

The developed model is applicable only to steel building up to 20 storeys. The feasibility of using artificial neural networks for conceptual design of bracings systems for tall steel buildings more than 20 storeys has not been investigated.

Implementation of the broad methodology outlined for the use of neural networks can be accomplished by conducting short training courses. This will provide personnel with flexibility in addressing buildings‐specifics bracing conditions and limitations.

In tall building design a lot of progress has been made in the development of software tools for numerical intensive tasks of analysis, design and optimization, however, professional software tools are not available to help the designer to choose an optimum building configuration at the conceptual design stage. The presented research provides a methodology to investigate the feasibility of using artificial neural networks for conceptual design of bracings systems for tall buildings. It is found that this approach for the selection of bracings in tall buildings is a better and cost effective option compared with database generated on the basis of expert opinion. It also correctly classifies and recommends the type of trussed bracing system.

Small- and medium-sized enterprises (SMEs) mainly rely on their structure and internal networks to achieve their goals and remain competitive. However, their limited internal capabilities and complex environments can hinder their stability. Thus, this study evaluated the relationships among specific factors toward fostering organizational resilience (OR) in tourism SMEs.

A multi-methodological approach was adopted to address this research study, including (1) social network analysis (SNA) to formulate the conceptual model and (2) construct validation through partial least squares path modeling (PLS-PM).

The six proposed hypotheses were supported. These results suggest that addressing these variables and relationships after considering management style and people development as critical factors can foster OR in tourism SMEs.

The ideas that were developed were constrained to the organizational domain. Although the results apply to the Mexican context, this limitation can be offset by extending the proposal to other emergent regions or organizations. This can also increase the generalization of the results and foster improvements in the approaches applied.

Academics and managers must rethink resilience as the final state generated by multiple factors. This requires reconfiguring inner organizational interactions, providing more autonomy to operative units, reinforcing business intelligence and improving feedback mechanisms.

This research study contrasts previous studies because it proposes that SNA be exploited to avail of the advantages it confers in designing the conceptual model. In this regard, we present new relationships to promote OR and provide new avenues in order to improve the analysis of adaptation processes.

Innovation in services is thought to be multi‐dimensional in nature, and in this context the purpose of this paper is to present and operationalise the concept of “elevated service offerings” (ESO) in collaborating service organisations. ESO stands for new or enhanced service offerings which can only be eventuated as a result of partnering, and which could not be delivered on individual organisational merit. ESO helps us expand our understanding of service innovation to include a service network or service system's dimension.

A structural equation model is specified and estimated based on constructs and relationships grounded in the literature, as well as self‐developed constructs, using empirical data from 449 respondents in an Australian telecommunications service provider (SP) and its partnering organisations.

Results show that ESO is a multi‐dimensional construct which was operationalised and validated through an extensive literature review, exploratory factor analysis, confirmatory factor analysis, and structural equation modelling using a holdout sample.

Qualitative and empirical data analysis was undertaken with data collected from a single large telecommunications SP organisation, and its partnering organisations. Future research may seek to collect data from the entire telecommunications industry sector and their partnering organisations, across other service sectors, or even any other organisation where collaboration is pivotal to their success.

Service organisations today need to understand that innovation in services is not just about process or product innovation, or even performance and productivity improvements, but in fact includes organisational forms of innovation. Indeed, the interactions and complementarities between the three different aspects of ESO – strategic, productivity, and performance – highlight the increasing complex and multi‐dimensional character of innovation and the ongoing iterative process.

This research provides empirical evidence for the existence of a multi‐dimensional innovation in services construct – known as elevated service offerings in a collaborative service network, along with an adapted definition of service and a service innovation model.

For the case of many content features, This paper aims to investigate which content features in video and text ads more contribute to accurately predicting the success of crowdfunding by comparing prediction models.

With 1,368 features extracted from 15,195 Kickstarter campaigns in the USA, the authors compare base models such as logistic regression (LR) with tree-based homogeneous ensembles such as eXtreme gradient boosting (XGBoost) and heterogeneous ensembles such as XGBoost + LR.

XGBoost shows higher prediction accuracy than LR (82% vs 69%), in contrast to the findings of a previous relevant study. Regarding important content features, humans (e.g. founders) are more important than visual objects (e.g. products). In both spoken and written language, words related to experience (e.g. eat) or perception (e.g. hear) are more important than cognitive (e.g. causation) words. In addition, a focus on the future is more important than a present or past time orientation. Speech aids (see and compare) to complement visual content are also effective and positive tone matters in speech.

This research makes theoretical contributions by finding more important visuals (human) and language features (experience, perception and future time). Also, in a multimodal context, complementary cues (e.g. speech aids) across different modalities help. Furthermore, the noncontent parts of speech such as positive “tone” or pace of speech are important.

Founders are encouraged to assess and revise the content of their video or text ads as well as their basic campaign features (e.g. goal, duration and reward) before they launch their campaigns. Next, overly complex ensembles may suffer from overfitting problems. In practice, model validation using unseen data is recommended.

Rather than reducing the number of content feature dimensions (Kaminski and Hopp, 2020), by enabling advanced prediction models to accommodate many contents features, prediction accuracy rises substantially.

The purpose of this paper is to shed light on improving value creation from intellectual capital (IC) through reducing causal ambiguity and finding effective IC interventions.

First, several guiding rules demonstrating the contribution of system dynamics (SD) to the field of IC management are introduced. Second, evidence for modelling resource dynamics is provided across a knowledge-based industry, insurance. Third, a management problem of an insurance company is modelled and then simulated using SD tools to monitor and improve the alignment of key resources with the firm’s market growth strategy.

The modelling and further simulation practice demonstrated the advantages of applying SD for analysing resource management problems to identify the critical IC components, intervention points and decision rules that may stimulate value-creating loops. Specifically for the case of an insurance company’s failure in market growth, it led to recognising the critical role of agency sales productivity as a key component of company’s relational capital and the intellectual liabilities that can lead to value destruction.

Reducing causal ambiguity in IC value creation through modelling and simulating firm resource dynamics is the main contribution of this paper. It enables finding the best intervention points for developing IC-based initiatives to stimulate value-creation mechanisms, as well identifying possible points of value destruction.

The plain High Dimensional Model Representation (HDMR) method needs Dirac delta type weights to partition the given multivariate data set for modelling an interpolation problem. Dirac delta type weight imposes a different importance level to each node of this set during the partitioning procedure which directly effects the performance of HDMR. The purpose of this paper is to develop a new method by using fluctuation free integration and HDMR methods to obtain optimized weight factors needed for identifying these importance levels for the multivariate data partitioning and modelling procedure.

A common problem in multivariate interpolation problems where the sought function values are given at the nodes of a rectangular prismatic grid is to determine an analytical structure for the function under consideration. As the multivariance of an interpolation problem increases, incompletenesses appear in standard numerical methods and memory limitations in computer‐based applications. To overcome the multivariance problems, it is better to deal with less‐variate structures. HDMR methods which are based on divide‐and‐conquer philosophy can be used for this purpose. This corresponds to multivariate data partitioning in which at most univariate components of the Plain HDMR are taken into consideration. To obtain these components there exist a number of integrals to be evaluated and the Fluctuation Free Integration method is used to obtain the results of these integrals. This new form of HDMR integrated with Fluctuation Free Integration also allows the Dirac delta type weight usage in multivariate data partitioning to be discarded and to optimize the weight factors corresponding to the importance level of each node of the given set.

The method developed in this study is applied to the six numerical examples in which there exist different structures and very encouraging results were obtained. In addition, the new method is compared with the other methods which include Dirac delta type weight function and the obtained results are given in the numerical implementations section.

The authors' new method allows an optimized weight structure in modelling to be determined in the given problem, instead of imposing the use of a certain weight function such as Dirac delta type weight. This allows the HDMR philosophy to have the chance of a flexible weight utilization in multivariate data modelling problems.

The purpose of this paper is to analyze the difficulties observed during implementation of quality management systems (QMSs) in Brazilian manufacturing companies.

The methodological strategy used was a literature review, a panel of experts and a survey. Through the literature review, 15 difficulties associated with the implementation of QMS were raised; these were organized into latent variables by specialists in the subject and served as the basis for a survey. In total, 123 professionals working with quality management in manufacturing companies participated in the research and the data collected were analyzed by means of second-order confirmatory factorial analysis.

The results allowed the validation of the 15 difficulties observed in the literature, and it was evidenced that these difficulties are structured in four latent variables as follows: difficulties associated with employees; difficulties associated with QMS structuration; difficulties associated with integration; and difficulties resulting from the planning.

The main limitation of this research is the sample size, because 123 professionals that work with quality management in manufacturing companies participated in the research. It should be noted, however, that all parameters evaluated through the second-order confirmatory factorial analysis were validated.

The findings have great value for both quality management professionals, who may use those findings to guide the pre-implementation phase of a QMS, and researchers, who may use those findings as a foundation for future studies, in the development of models or tools related to QMS implementation.

No other paper was found on the scientific basis with the same focus for Brazilian manufacturing companies, thus demonstrating originality. The value of the research lies in the fact that the results presented here, statistically validated, may be used by other researchers and market professionals.

The purpose of this paper is to develop a brief outcome expectancies questionnaire applicable across nicotine, alcohol, opioid and stimulant users seeking or willing to seek treatment and to assess its construct and predictive validity.

The items were generated using semi-structured interviews. A cross-sectional study was used to determine the factor structure and internal reliability, to compare the factor structure across the groups and to assess construct validity. Scores were used to predict reduction in dependence at three-month follow-up.

The qualitative study produced 98 items. For the cross-sectional study 99 nicotine, 96 alcohol, 98 opioid and 77 stimulant misusers were recruited. Factor analysis produced a two-factor (positive and negative expectancies) solution, similar across groups. A 28-item common version had scale correlations above 0.94 with the long versions of each group, and high internal consistency (Cronbach's α>0.90). The Positive expectancies sub-scale was positively correlated with urges across all groups, and negatively correlated with self-efficacy in three groups. Negative sub-scale scores were positively correlated with motivation sub-scales and self-efficacy in three groups. Urges and negative expectancies predicted reduction of dependence at three months.

The study suggested that outcome expectancies are similar across substance sub-groups. The new tool appears to have good construct and predictive validity. Further validation with larger samples is required.

This is the first tool to measure outcome expectancies across substances, facilitating relevant research with poly-substance users.