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The author posits that the management model of an organization determines what kind of business models can be pursued within that organization and that successful 21st century management models are very different from those that succeeded in the 20th century.

The author compares and contrasts successful 21st century management models with models that succeeded in the 20th century.

Success in the digital age requires a 21st century management model and mindset based on an obsession with delivering value to customers.

The management model incorporates the key ‘written and unwritten rules’ of the firm. The success of digital innovation can be threatened by 20th Century management assumptions that thwart Agile initiatives.

Article explains how Agile mindsets and practices are essential to the 21st century management model, and how they potentiate the firm’s focus on creating customers.

Organizations use Industry 4.0 readiness models to evaluate their preparedness prior to the implementation of Industry 4.0. Though there are many studies on Industry 4.0 readiness models, the dimensions of readiness differ. Besides, there is no study empirically validating the readiness model in different sectors or types of organization. The purpose of this study is to conceptualize the dimensions of the Industry 4.0 readiness model and subsequently evaluate the criticality of these dimensions in manufacturing, service, small and medium-sized enterprises (SMEs) and large enterprises (LEs).

The study uses an exploratory sequential mixed method design. In phase one, 37 senior managers participated through a purposive sampling frame. In phase two, 70 senior managers participated in an online survey.

The results of the study indicated that the Industry 4.0 readiness model has 10 dimensions. Further, the criticality of the dimensions as applied to different sectors and type of organizations is put forward. This study will help manufacturing, services, SMEs and LEs to evaluate Industry 4.0 readiness before commencing the deployment of Industry 4.0.

The findings can be very beneficial for Industry 4.0 practitioners and senior managers in different organisations to understand what readiness dimensions need to be considered prior to implementation of Industry 4.0 technology.

This paper makes an attempt to conceptualize the Industry 4.0 readiness model and utilizes an exploratory mixed method for critically evaluating the dimensions related to the model.

Multiple input multiple-output (MIMO) has emerged as one among the many noteworthy technologies in recent wireless applications because of its powerful ability to improve bandwidth efficiency and performance, i.e. through developing its unique spatial multiplexing capability and spatial diversity gain. For carrying out an enhanced communication in next-generation networks, the MIMO and orthogonal frequency division multiple systems were combined that facilitate the spatial multiplexing on resource blocks (RBs) based on time-frequency. This paper aims to propose a novel approach for maximizing the throughput of cell-edge users and cell-center users.

In this work, the specified multi-objective function is defined as the single objective function, which is solved by the introduction of a new improved algorithm as well. This optimization problem can be resolved by the fine-tuning of certain parameters such as assigned power for RB, cell-center user, cell-edge user and RB allocation. The fine-tuning of parameters is attained by a new improved Lion algorithm (LA), termed as Lion with new cub generation (LA-NCG) model. Finally, the betterment of the presented approach is validated over the existing models in terms of signal to interference plus noise ratio, throughput and so on.

On examining the outputs, the adopted LA-NCG model for 4BS was 66.67%, 66.67% and 20% superior to existing joint processing coordinated multiple point-based dual decomposition method (JC-DDM), fractional programming (FP) and LA models. In addition, the throughput of conventional JC-DDM, FP and LA models lie at a range of 10, 45 and 35, respectively, at the 100th iteration. However, the presented LA-NCG scheme accomplishes a higher throughput of 58. Similarly, the throughput of the adopted scheme observed for 8BS was 59.68%, 44.19% and 9.68% superior to existing JC-DDM, FP and LA models. Thus, the enhancement of the adopted LA-NCG model has been validated effectively from the attained outcomes.

This paper adopts the latest optimization algorithm called LA-NCG to establish a novel approach for maximizing the throughput of cell-edge users and cell-center users. This is the first that work uses LA-NCG-based optimization that assists in fine-tuning certain parameters such as assigned power for RB, cell-center user, cell-edge user and RB allocation.

This study aims to explore the digital divide between students living in metropolitan and non-metropolitan areas in the Antioquia region of Colombia. This is achieved by collecting data about student interactions from the Moodle learning management system (LMS), and subsequently applying supervised machine learning models to infer the gap between students in metropolitan and non-metropolitan areas.

This work uses the well-established Cross-Industry Standard Process for Data Mining methodology, which comprises six phases, viz., problem understanding, data understanding, data preparation, modelling, evaluation and implementation. In this case, student data was collected from the Moodle platform from the Antioquia campus of the UNAD distance learning university.

The digital divide is evident in the classification model when observing differences in variables such as the number of accesses to the LMS, the total time spent and the number of distinct IP addresses used, as well as the number of system modification events.

This study provides conclusions regarding the problems students in virtual education may face as a result of the digital divide in Colombia which have become increasingly visible since the implementation of machine learning methodologies on LMS such as Moodle. However, these practices may be replicated in any virtual educational context and furthermore be extended to enable personalisation of various aspects of the Moodle platform to meet the individual needs of students.

Food delivery apps (FDA) have been widely adopted by customers in online-to-offline (O2O) catering businesses. This study aims to explore the mechanism regarding the stickiness of FDA and indicates why customers have the intention to reuse them.

A research model was developed based on the e-commerce system successful model (ECSS model) and social influence theory. Using the data collected from 312 customers who have FDA usage experience via an online survey, the established model was empirically assessed by partial least squares based structural equation model.

The results show that factors including information quality, ease of use, convenience and various choices perceived by FDA users are significant antecedents of customer satisfaction and perceived value, which in turn positively influence customers' intention to reuse. Besides, informational social influence and normative social influence play important roles in directly or indirectly affecting customers' intention to reuse.

This study extends the e-commerce system success model and enriches the literature regarding stickiness of FDA. Besides, the understanding of social influence in FDA usage has been deepened by addressing its role in the ECSS model based on the features and contexts of such apps.

The purpose of this paper is to achieve effective governance of online rumors through the proposed rumor propagation model and immunization strategy.

The paper leverages the agent-based modeling (ABM) method to model individuals from two aspects, behavior and attitude. Based on the analysis and research of online data, we propose a rumor propagation model, namely the Untouched view transmit removed-Susceptible hesitate agree disagree (Unite-Shad), and devise an immunization strategy, namely the Gravity Immunization Strategy (GIS). A graph-based framework, namely Pregel, is used to carry out the rumor propagation simulation experiments. Through the experiments, the rationality of the Unite-Shad and the effectiveness of the GIS are verified.

The study discovers that the inconsistency between human behaviors and attitudes in rumor propagation can be explained by the Unite-shad model. Besides, the GIS, which shows better performance in small-world networks than in scale-free networks, can effectively suppress rumor propagation in the early stage.

This paper provides an effective immunization strategy for rumor governance. Specifically, the Unite-Shad model reveals the mechanism of rumor propagation, and the GIS provides an effective governance method for selecting immune nodes.

The inconsistency of human behaviors and attitudes in real scenes is modeled in the Unite-Shad model. Combined with the model, the definition of diffusion domain is proposed and a novel immunization strategy, namely GIS, is designed, which is significant for the social governance of rumor propagation.

Open government data (OGD) is a comparatively new field in e-government and the factors influencing its continuance use by citizens have not been extensively explored. A better understanding of these factors can help the government to articulate strategies and policies that can advance the acceptance and use of OGD technologies. Thus, this paper aims to empirically determine the predictors influencing the continuance usage intention of OGD technologies.

Following an empirical investigation among 370 respondents in Bangladesh, a developing country, the paper applied path analysis using the structural equation modeling approach. The unified theory of acceptance and use of the technology model is integrated with the information system continuance model to investigate the continuance usage intention of OGD technologies.

The outcomes of this study reveal that performance expectancy, effort expectancy, social influence and facilitating conditions (FC) directly affect users’ satisfaction (SAT). In addition, SAT and FC were found statistically significant toward continuance usage intention of OGD technologies.

The findings of this study suggest policymaker and OGD providers to formulate or modify their strategies to retain the existing OGD users and stimulate persistence usage.

Facilitating long-term use by citizens would increase their engagement and they might derive value from the OGD platforms. Concurrently, the government’s objective of ensuring increased future use of OGD technologies would be better realized.

The novelty of this study lies in the fact that it addresses a previously overlooked area of open data research, namely, the acceptance and use of open data technologies and ways to stimulate it. This study has contributed to the existing but limited literature on continuance usage intention of OGD technologies in the context of a developing country.

The purpose of this paper is to investigate the effect of snow on the radio link performance of wireless sensor nodes in Indian Himalayan conditions and to propose empirical path loss models for radio wave propagation.

At the remote test site, one source and three listening wireless sensor nodes were deployed at frequency of 433 MHz. The path loss models are derived from experimental data collected during the period of snowfall and clear weather conditions. Linear, exponential, second and third-order polynomials path loss models have been investigated along with experimental data.

With the help of curve fitting and goodness-of-fit tests, it is found that path loss can be modelled through third-order polynomial equation during the snowfall period. However, if sensor is buried, the acceptable path loss model is exponential. Similarly, for unified modelling requirement, exponential path loss model over linear can be a preferred choice.

Results show that path loss can be estimated priori for deciding optimum transmission energy in wireless sensor network. Presented work is usable in extending the lifetime of health monitoring devices buried in snowy environment.

The presence of air in the water flow over the hydrofoil is investigated. The examined hydrofoil is ClarkY 11.7% with an angle of attack of 8 deg. The flow simulations are performed with the assumption of different models. The Singhal cavitation model and the models which resolve the non-condensable gas including 2phases and 3phases are implemented in the numerical model. The calculations are performed with the uRANS model with assumption of the constant temperature of the mixture. The two-phase flow is simulated with a mixture model. The dynamics and structures of cavities are compared with literature data and experimental results.

The cavitation regime can be observed in some working conditions of turbomachines. The phase transition, which appears on the blades, is the source of high dynamic forces, noise and also can lead to the intensive erosion of the blade surfaces. The need to control this process and to prevent or reduce the undesirable effects can be fulfilled by the application of non-condensable gases to the liquid.

The results show that the Singhal cavitation model predicts the cavity structure and related characteristics differently with 2phases and 3phases models at low cavitation number where the cavitating flow is highly dynamic. On the other hand, the impact of dissolved air on the cloud structure and dynamic characteristic of cavitating flow is gently observable.

The originality of this paper is the evaluation of different numerical cavitation models for the prediction of dynamic characteristics of cavitating flow in the presence of air.

The purpose of this paper is to propose an approach for managing relevant factors and activities for implementing data governance in an organization. The process of assessing the establishment of data governance in an organization is intrinsically imprecise, due to the characteristics of new problem settings, particularly in relation to newly generated alternatives or vaguely defined qualitative assessment criteria.

To reject the inherent subjectiveness and imprecision involved in the evaluation process, the authors use the concept of fuzzy logic in this approach for developing the assessment model and analyzing the model for allocating the management efforts in the most efficient way to improve the data governance deployment level.

This paper identifies relevant factors and activities for implementing data governance in an organization and evaluates the state of data governance based on causal relationships between influential factors. In this study, factors are prioritized for effective allocation of limited management efforts in any improvement plan.

The interrelationships among factors are contextual and based on the perceptions of experts who may be biased as per their background and area of expertise. Meanwhile, lack of a data governance plan may cause failure during its implementation in an organization, as the worth of an organization's data will not be determined precisely. The paper has tremendous practical implications for organizations that intend to implement the data governance program and evaluate its state to design an improvement plan.

The paper proposes an approach for implementing data governance in an organization faced with limited resources for improvement.