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This study examines the direct and indirect effects of organizational environmental orientation (EO)/culture, quality management practices (QMP) and sustainability experience (SE) on the relationship between organizational green practices (GP) and the triple bottom line (TBL) of sustainability performance (SuP).

To test the seven hypotheses, a structured questionnaire was used to collect data. The responses of 365 managers from various USA businesses in the service industries were analyzed using IBM SPSS and structural equation modeling (SEM)-AMOS.

The empirical results indicate that positive SuP in the economic, environmental and social dimensions and organizational GP can be improved by a strong culture of EO, effective QMP and substantial SE.

This research fills the gap in existing research between important organizational and environmental priorities and SuP. Consequently, the study provides managers with important strategic guidance: for environmental practices to achieve profitability and sustainability success, companies must promote an environmental-mindful culture and strategically invest in integrated QM systems.

This research is one of the first that explores how organizational environmental culture and QMP affect directly and indirectly the relationship between GP and SuP. These results provide empirical evidence to support the claim that environmental culture and QMP have significant direct and indirect effects on the relationship between GP and SuP dimensions.

This study aims to examine the direct and indirect effects of organizational culture (OC) and total quality management practices (TQMPs) on the relationship between green practices (GPs) and sustainability performance (SP) by using structural equation modeling (SEM) analysis.

This study proposed a conceptual research model of the relationships and formulated six hypotheses. This study used a structured questionnaire based on previous studies to collect relationship data to test these hypotheses, and 441 full-time managers from various US businesses responded. The complete and valid survey responses were then tested against the hypotheses using IBM SPSS Statistics and SEM-AMOS.

Results supported the relationships proposed in the research model. They indicated that a strong supporting OC and TQMPs might improve positive SP and GPs. Additionally, the more managers are aware of their companies' GPs, the more likely they will feel positive about the organization's SP.

A larger sample size to ensure statistically minimum representation in several major industries would better validate the findings and help identify significant differences in industry-specific OCs, TQMPs, GPs and SPs. Similarly, ensuring a varied geographical representation (both within the USA and internationally) would help determine if the findings vary according to the respondent's location. Furthermore, collecting the data during Year 1 of the COVID-19 pandemic may have skewed the results. Thus, once the working environment has been normalized, the survey should be repeated to determine if the findings are valid post-pandemic.

The findings of this study provide important strategic guidance for managers who work to balance the implementation of corporate GPs and the triple bottom line dimensions of SP. For practitioners, the results showed that companies could accomplish both profitability and sustainability if they are willing to continuously pay attention to environmental issues and strategically invest in cost-efficient and eco-friendly initiatives.

To the best of the authors’ knowledge, this research is one of the first to explore how OC and TQMPs, directly and indirectly, affect the relationship between GPs and the triple bottom line dimensions of SP. These results imply that OC and TQMPs have a significant indirect impact on the relationship between GPs and the SP dimensions.

The purpose of this study is to examine the direct and indirect effects of organizational culture and quality improvement practices on the relationship between green practices and sustainability performances by using the structural equation modeling (SEM) approach.

Structured questionnaires were used to collect data from 330 full-time managerial employees in established businesses. The relationships depicted in the proposed conceptual model were tested through six hypotheses. IBM SPSS Statistics and SEM-AMOS were used to test the hypotheses.

The results supported proposed relationships in the model, except the one between organizational culture and green practices. The findings indicate that the more employees are aware of companies’ green practices, the more likely they are going to feel positive about the sustainability performance of the organization. Additionally, a strong organizational culture that supports the green movement and quality improvement practices lead to positive sustainability outcomes.

A larger sample size in several major industries would allow the development of industry-specific sustainability strategies and best practices. In addition, a study based in a different geographical location in the USA or in a different country is recommended to see if the findings can be generalized across different industries, geographical locations or countries.

Sustainability has undoubtedly become a major concern for many companies today. The findings of this study provide important guidelines for practitioners to balance the implementation of green practices, organizational culture, quality improvement practices and sustainability performance dimensions. Profitability and sustainability can be achieved together when companies are willing to continuously pay attention to environmental issues and strategically invest in initiatives that are cost-efficient and eco-friendly.

This study is one of the first that explores how organizational culture and quality improvement practices affect directly and indirectly the relationship between green practices and sustainability performance. The results imply that organizational culture and quality improvement practices significantly affect the relationship between green practices and the sustainability performance dimensions.

The paper aims to answer the following questions. Is marketing requisitely holistic? Marketing serves managers, governors, owners and employees as well as customers, suppliers and other stakeholders with its activities in order to help company increase well‐being of stakeholders. What about the broader society's well‐being and future? What will follow, once the innovative‐society phase of socio‐economic development creates affluence, which diminishes human ambition to work in order to have? Social responsibility might be the next step in achieving success.

There are new forms of marketing (e.g. societal marketing; relationship marketing; cause‐related marketing; and green marketing) that could help humans accomplishing this task, to some extent. Marketing will have to detect, elaborate and disseminate new data, along with using them for its action; the paper does not tackle the latter, but marketing taking into account the social responsibility of the company in order to help companies.

Companies will namely need more/requisitely holistic bases to develop innovative products, acceptable with social responsibility. Experience says that ethnographers, anthropologists, and other social scientists are becoming necessary in the “open innovation” model and the extremely demanding market of the affluent and nearly affluent society. So is a more systemic/holistic thinking and action of companies, including their marketing.

The paper suggests how marketing must adapt to meet new challenges.

The purpose of this paper is to aggregate different preference information in group decision‐making process such as interval preference order, interval utility value, interval number reciprocal comparison matrix, and interval number complementary comparison matrix.

First, the consistency definitions of four kinds of uncertain preference information are defined. Then, the upper‐ and low errors are introduced to solve the inconsistent decision‐making case. Following that, the weight model for each uncertain preference is proposed, respectively.

The aggregation approach based on minimal group deviation errors is suggested in order to obtain the utmost consistent opinion. In addition, the consistency judgment level and consistency extent are defined owing to the aggregation result.

The calculation scale is large, if many decision makers will attend group decision‐making process.

A very useful approach for aggregation of the different preference in group decision‐making case.

Because of differences in knowledge structure, judgment level, and individual preference, decision makers express their judgment preferences via differently structured decision‐making processes. Owing to the complexity and uncertainty of decision‐making problems and the fuzziness of human thought, it is unrealistic to depict complex problems in the certain preference style. For decision‐making preference structures, group decision‐making aggregation approaches include the aggregation on the same kind of preference structure and the different kinds of preference structures. The study on the aggregation of the same kind of preference structure has received a deal of attention, but study into the aggregation of the different kinds of uncertainty preference structures is still a new field.

To demonstrate the scalability of the genetic hybrid algorithm (GHA) in monitoring a local neural network algorithm for difficult non‐linear/chaotic time series problems.

GHA is a general‐purpose algorithm, spanning several areas of mathematical problem solving. If needed, GHA invokes an accelerator function at key stages of the solution process, providing it with the current population of solution vectors in the argument list of the function. The user has control over the computational stage (generation of a new population, crossover, mutation etc) and can modify the population of solution vectors, e.g. by invoking special purpose algorithms through the accelerator channel. If needed, the steps of GHA can be partly or completely superseded by the special purpose mathematical/artificial intelligence‐based algorithm. The system can be used as a package for classical mathematical programming with the genetic sub‐block deactivated. On the other hand, the algorithm can be turned into a machinery for stochastic analysis (e.g. for Monte Carlo simulation, time series modelling or neural networks), where the mathematical programming and genetic computing facilities are deactivated or appropropriately adjusted. Finally, pure evolutionary computation may be activated for studying genetic phenomena. GHA contains a flexible generic multi‐computer framework based on MPI, allowing implementations of a wide range of parallel models.

The results indicate that GHA is scalable, yet due to the inherent stochasticity of neural networks and the genetic algorithm, the scalability evidence put forth in this paper is only indicative. The scalability of GHA follows from maximal node intelligence allowing minimal internodal communication in problems with independent computational blocks.

The paper shows that GHA can be effectively run on both sequential and parallel platforms. The multicomputer layout is based on maximizing the intelligence of the nodes – all nodes are provided with the same program and the available computational support libraries – and minimizing internodal communication, hence GHA does not limit the size of the mesh in problems with independent computational tasks.

To create new hybrid algorithms that combine boosting and support vector machines to outperform other known algorithms in selected contexts of binary classification problems.

Support vector machines (SVM) are known in the literature to be one of the most efficient learning models for tackling classification problems. Boosting algorithms rely on other classification algorithms to produce different weak hypotheses which are later combined into a single strong hypothesis. In this work the authors combine boosting with support vector machines, namely the AdaBoost.M1 and sequential minimal optimization (SMO) algorithms, to create new hybrid algorithms that outperform standard SVMs in selected contexts. This is achieved by integration with different degrees of coupling, where the four algorithms proposed range from simple black‐box integration to modifications and mergers between AdaBoost.M1 and SMO components.

The results show that the proposed algorithms exhibited better performance for most problems experimented. It is possible to identify trends of behavior bound to specific properties of the problems solved, where one may hence apply the proposed algorithms in situations where it is known to succeed.

New strategies for combining boosting and SVMs may be further developed using the principles introduced in this paper, possibly resulting in other algorithms with yet superior performance.

The hybrid algorithms proposed in this paper may be used in classification problems with properties that they are known to handle well, thus possibly offering better results than other known algorithms in the literature.

This paper introduces the concept of merging boosting and SVM training algorithms to obtain hybrid solutions with better performance than standard SVMs.

The need for maximum cognition from massive amounts of data is becoming explosive. Different visualization mechanisms are being introduced to achieve this aim. This paper aims to consider the state of organizations as regards being abreast with the recent visualization technologies.

In this paper, the authors look critically at the system adoption life cycle as it applied to visualization system. Also, the two forms of visualization; static and dynamic are looked at, considering the least applied and why. An evaluation of the visualization system was implemented in order to discern if it provides any benefit to organizations.

The study showed that the RT‐DANGO tool when compared to result from Excel exposed some spikes at some points when visualized dynamically which are not observed using usual static graph as it exposed some unseen events. The mean time taken to finish analysis of 4,000 concurrent‐related data were 168 s using RT‐DANGO and 121 s with the MS‐Excel. The difference was significant to p<0.05 (Z=−2.040). This was considered a reasonable tradeoff for accuracy, clarity and completeness of information.

The paper shows the factors that are responsible for visualization's usage in organizations. The paper will thus serve as an eye opener to critical issues affecting the visualization industry to the benefit of all stakeholders. Some references are made to the challenges faced in Nigeria.