scholarly journals Predicting Product Uptake Using Bass, Gompertz, and Logistic Diffusion Models: Application to a Broadband Product

2020 ◽  
Vol 9 (2) ◽  
pp. 5
Author(s):  
Franklin M. Lartey
Keyword(s):  
Ordinary Least Squares ◽  
Diffusion Models ◽  
Training Data ◽  
Future Research ◽  
Internet Service ◽  
Fiber To The Home ◽  
Internet Connections ◽  
Bass Diffusion Model ◽  
Positive Return ◽  
Best Fit

In today’s competitive environment, broadband companies innovate to stay competitive, retain existing customers, and attract new customers. A recent innovative product in this industry is the deployment of the gigabit Internet service over fiber optic networks as a solution to the growing bandwidth demands from consumers. One determinant of the decision to deploy such product is the expectation of a positive return on investment (ROI) determined among others by the penetration or take rate of the product or service. Like any product, the adoption of the gigabit Internet is influenced by the reaction of customers to this innovation. Some customers are early adopters of the product while others might not be interested in higher bandwidth Internet connections or will simply adopt the product at a later time. The purpose of this paper was to identify a model that best predicts future trends in the uptake of the gigabit Internet product over fiber-to-the home (FTTH). To that effect, this study implemented three different models: Bass, Gompertz, and logistic diffusion models; analyzed their predictive abilities; and determined the best fit model in a FTTH brownfield scenario. The data used for the study were split into two sets: the first or training set was used to create the models and the second was used to validate their predicting abilities. The data analysis used the ordinary least squares (OLS) method to select the best fit model. The results suggested that while Gompertz best fitted the training data, Bass had a better forecasting power. In other words, the Bass diffusion model was best at forecasting future uptake of the gigabit Internet service, while Logistic optimistically forecasted above the take rate and Gompertz pessimistically forecasted below. These findings present various implications for researchers and practicians. For example, future research could replicate the study for different industries and products, while practicians could anticipate realistic financial results from the implementation of the findings.

scholarly journals Artificial Intelligence, 3D Documentation, and Rock Art—Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images

2021 ◽  
Author(s):  
Christian Horn ◽  
Oscar Ivarsson ◽  
Cecilia Lindhé ◽  
Rich Potter ◽  
Ashely Green ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bronze Age ◽  
Data Augmentation ◽  
Rock Art ◽  
Training Data ◽  
Future Research ◽  
Rock Surface ◽  
Southern Scandinavia ◽  
Bounding Boxes

AbstractRock art carvings, which are best described as petroglyphs, were produced by removing parts of the rock surface to create a negative relief. This tradition was particularly strong during the Nordic Bronze Age (1700–550 BC) in southern Scandinavia with over 20,000 boats and thousands of humans, animals, wagons, etc. This vivid and highly engaging material provides quantitative data of high potential to understand Bronze Age social structures and ideologies. The ability to provide the technically best possible documentation and to automate identification and classification of images would help to take full advantage of the research potential of petroglyphs in southern Scandinavia and elsewhere. We, therefore, attempted to train a model that locates and classifies image objects using faster region-based convolutional neural network (Faster-RCNN) based on data produced by a novel method to improve visualizing the content of 3D documentations. A newly created layer of 3D rock art documentation provides the best data currently available and has reduced inscribed bias compared to older methods. Several models were trained based on input images annotated with bounding boxes produced with different parameters to find the best solution. The data included 4305 individual images in 408 scans of rock art sites. To enhance the models and enrich the training data, we used data augmentation and transfer learning. The successful models perform exceptionally well on boats and circles, as well as with human figures and wheels. This work was an interdisciplinary undertaking which led to important reflections about archaeology, digital humanities, and artificial intelligence. The reflections and the success represented by the trained models open novel avenues for future research on rock art.

More on The Structure of Male Role Norms

1998 ◽  
Vol 22 (2) ◽  
pp. 135-155 ◽  
Author(s):  
Ann R. Fischer ◽  
David M. Tokar ◽  
Glenn E. Good ◽  
Andrea F. Snell
Keyword(s):  
Factor Model ◽  
Future Research ◽  
Validation Sample ◽  
Male Role ◽  
Confirmatory Factor Analyses ◽  
Total N ◽  
Masculinity Ideology ◽  
Male Role Norms ◽  
Role Norms ◽  
Best Fit

This study assessed the structure of a widely used measure of masculinity ideology, the Male Role Norms Scale (Thompson & Pleck, 1986), using data from four samples of male college students (total N= 656) at two large, public universities (one Midwestern, one Eastern-Central). Exploratory factor analysis suggested a four-factor model best fit the data in the exploratory sample (sample 1; N = 210). The four factors were Status/Rationality, Antifemininity, Tough Image, and Violent Toughness. A series of confirmatory factor analyses on a validation sample (samples 2, 3, and 4; N = 446), tested four models based on theory (i.e., Brannon, 1976) and previous research (i.e., Thompson & Pleck, 1986). Results from Study 1, our exploratory analysis, indicated that the four-factor model derived from the exploratory sample in Study 1 provided the best fit for the validation sample data of all models tested and also provided a good fit in absolute terms, according to several model–data fit indices. Implications for the assessment of masculinity ideology and suggestions for future research are discussed.

Automatic information retrievement for exporting services: First project findings from the development of an AI based export decision supporting instrument

2021 ◽  
pp. 2-11
Author(s):  
David Aufreiter ◽  
Doris Ehrlinger ◽  
Christian Stadlmann ◽  
Margarethe Uberwimmer ◽  
Anna Biedersberger ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Research Process ◽  
Training Data ◽  
Future Research ◽  
Manufacturing Companies ◽  
Market Information ◽  
Annotation Scheme ◽  
Export Decisions ◽  
Decision Supporting

On the servitization journey, manufacturing companies complement their offerings with new industrial and knowledge-based services, which causes challenges of uncertainty and risk. In addition to the required adjustment of internal factors, the international selling of services is a major challenge. This paper presents the initial results of an international research project aimed at assisting advanced manufacturers in making decisions about exporting their service offerings to foreign markets. In the frame of this project, a tool is developed to support managers in their service export decisions through the automated generation of market information based on Natural Language Processing and Machine Learning. The paper presents a roadmap for progressing towards an Artificial Intelligence-based market information solution. It describes the research process steps of analyzing problem statements of relevant industry partners, selecting target countries and markets, defining parameters for the scope of the tool, classifying different service offerings and their components into categories and developing annotation scheme for generating reliable and focused training data for the Artificial Intelligence solution. This paper demonstrates good practices in essential steps and highlights common pitfalls to avoid for researcher and managers working on future research projects supported by Artificial Intelligence. In the end, the paper aims at contributing to support and motivate researcher and manager to discover AI application and research opportunities within the servitization field.

scholarly journals A best-fit probability distribution for the estimation of rainfall in northern regions of Pakistan

2016 ◽  
Vol 11 (1) ◽  
pp. 432-440 ◽  
Author(s):  
M. T. Amin ◽  
M. Rizwan ◽  
A. A. Alazba
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Future Research ◽  
Maximum Rainfall ◽  
Type Iii ◽  
Goodness Of Fit Tests ◽  
Pearson Type ◽  
Northern Regions ◽  
Best Fit

AbstractThis study was designed to find the best-fit probability distribution of annual maximum rainfall based on a twenty-four-hour sample in the northern regions of Pakistan using four probability distributions: normal, log-normal, log-Pearson type-III and Gumbel max. Based on the scores of goodness of fit tests, the normal distribution was found to be the best-fit probability distribution at the Mardan rainfall gauging station. The log-Pearson type-III distribution was found to be the best-fit probability distribution at the rest of the rainfall gauging stations. The maximum values of expected rainfall were calculated using the best-fit probability distributions and can be used by design engineers in future research.

scholarly journals BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity Recognition

2020 ◽  
Author(s):  
Usman Naseem ◽  
Matloob Khushi ◽  
Vinay Reddy ◽  
Sakthivel Rajendran ◽  
Imran Razzak ◽  
...  
Keyword(s):  
State Of The Art ◽  
Language Model ◽  
Named Entity Recognition ◽  
Training Data ◽  
Entity Recognition ◽  
Future Research ◽  
Named Entity ◽  
Domain Specific ◽  
Context Dependent ◽  
Biomedical Named Entity Recognition

Abstract Background: In recent years, with the growing amount of biomedical documents, coupled with advancement in natural language processing algorithms, the research on biomedical named entity recognition (BioNER) has increased exponentially. However, BioNER research is challenging as NER in the biomedical domain are: (i) often restricted due to limited amount of training data, (ii) an entity can refer to multiple types and concepts depending on its context and, (iii) heavy reliance on acronyms that are sub-domain specific. Existing BioNER approaches often neglect these issues and directly adopt the state-of-the-art (SOTA) models trained in general corpora which often yields unsatisfactory results. Results: We propose biomedical ALBERT (A Lite Bidirectional Encoder Representations from Transformers for Biomedical Text Mining) - bioALBERT - an effective domain-specific pre-trained language model trained on huge biomedical corpus designed to capture biomedical context-dependent NER. We adopted self-supervised loss function used in ALBERT that targets on modelling inter-sentence coherence to better learn context-dependent representations and incorporated parameter reduction strategies to minimise memory usage and enhance the training time in BioNER. In our experiments, BioALBERT outperformed comparative SOTA BioNER models on eight biomedical NER benchmark datasets with four different entity types. The performance is increased for; (i) disease type corpora by 7.47% (NCBI-disease) and 10.63% (BC5CDR-disease); (ii) drug-chem type corpora by 4.61% (BC5CDR-Chem) and 3.89 (BC4CHEMD); (iii) gene-protein type corpora by 12.25% (BC2GM) and 6.42% (JNLPBA); and (iv) Species type corpora by 6.19% (LINNAEUS) and 23.71% (Species-800) is observed which leads to a state-of-the-art results. Conclusions: The performance of proposed model on four different biomedical entity types shows that our model is robust and generalizable in recognizing biomedical entities in text. We trained four different variants of BioALBERT models which are available for the research community to be used in future research.

Neighborhood Social Cohesion and Mobility Limitations Among Community-dwelling Older Americans: The Mediating Roles of Depressive Symptoms and Mastery

2021 ◽  
pp. 009141502110376
Author(s):  
Fei Wang ◽  
Weidi Qin ◽  
Jiao Yu
Keyword(s):  
Depressive Symptoms ◽  
Social Cohesion ◽  
Ordinary Least Squares ◽  
Community Dwelling ◽  
Neighborhood Environment ◽  
Future Research ◽  
Social Aspect ◽  
Mobility Limitations ◽  
Cross Sectional ◽  
Neighborhood Social Cohesion

Neighborhood environment plays an important role in late-life health; yet, the social aspect of neighborhood environment and its impact on mobility limitations have rarely been examined. This nonexperimental, cross-sectional study examines the relationship between neighborhood social cohesion and mobility limitations and the potential mediators (i.e., depressive symptoms, mastery) of this relationship. A total of 8,317 Americans aged 65 years and older were selected from the Health and Retirement Study. Using ordinary least squares regressions, this study shows that neighborhood social cohesion was negatively associated with mobility limitations ( B  =  −0.04, p < .01). A Sobel test of mediation indicated that this relationship was significantly mediated by depressive symptoms ( z  =  −9.10, p < .001) and mastery ( z  =  −8.86, p < .001). Findings suggest that neighborhood cohesion can reduce mobility limitations through mitigating depressive symptoms and increasing mastery. Future research should disentangle the temporal ordering of the mediators.

Metric Methods in Data Mining

2008 ◽  
pp. 849-879
Author(s):  
Dan A. Simovici
Keyword(s):  
Data Mining ◽  
Metric Space ◽  
Training Data ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Future Research Directions ◽  
Major Data ◽  
Geometric Study

This chapter presents data mining techniques that make use of metrics defined on the set of partitions of finite sets. Partitions are naturally associated with object attributes and major data mining problem such as classification, clustering, and data preparation benefit from an algebraic and geometric study of the metric space of partitions. The metrics we find most useful are derived from a generalization of the entropic metric. We discuss techniques that produce smaller classifiers, allow incremental clustering of categorical data and help user to better prepare training data for constructing classifiers. Finally, we discuss open problems and future research directions.

An intervariable approach to customer satisfaction and loyalty in the internet service market

2019 ◽  
Vol 29 (4) ◽  
pp. 772-798 ◽  
Author(s):  
Mamoun N. Akroush ◽  
Bushra K. Mahadin
Keyword(s):  
Customer Satisfaction ◽  
Customer Value ◽  
Perceived Value ◽  
Future Research ◽  
The Internet ◽  
Service Market ◽  
Internet Service ◽  
Confirmatory Factor Analyses ◽  
Content Type ◽  
Attitudinal Loyalty

Purpose The purpose of this paper is to examine a multidimensional model of customer perceived value (CPV), customer satisfaction (CS) and loyalty from internet subscribers’ perspectives. Design/methodology/approach In total, 1,297 out of 2,000 online surveys were valid for the analysis. Confirmatory factor analyses were performed to assess the research constructs’ unidimensionality, validity and composite reliability. Structural path analysis was used to test the hypothesized relationships of the research model. Findings CPV positively affects functional and technical satisfaction as well as cognitive loyalty. Functional satisfaction positively affects technical satisfaction and attitudinal loyalty. Attitudinal loyalty positively affects cognitive and behavioral loyalty, and the latter positively affects cognitive loyalty. In total, 53 percent of variation in cognitive loyalty was caused by behavioral, attitudinal loyalty and perceived value path. Research limitations/implications Future research could investigate other outcomes of CS dimensions, such as customer lifetime value, customer retention, profitability, return on investment and market share, and their effects on customer loyalty (CL). Future research can also examine the effect of other dimensions of perceived customer value on CS and loyalty dimensions simultaneously. Other future research areas are also outlined. Practical implications CPV acts as a cornerstone to developing a successful multidimensional program of CL through functional and technical satisfactions. Marketing directors need to focus on building CL schemes and strategies that should take into consideration the long-term and short-term loyalty. Originality/value Theoretically, using an intervariable perspective, this paper has responded to important calls for conducting research on the chain of perceived value, CS and loyalty chain. Practically, this paper is the first empirical research devoted to developing an intervariable approach to the chain of perceived value, CS and loyalty in the internet service market.

Police officers’ role orientations

2019 ◽  
Vol 42 (5) ◽  
pp. 944-959 ◽  
Author(s):  
Jacinta M. Gau ◽  
Nicholas D. Paul
Keyword(s):  
Law Enforcement ◽  
Work Environment ◽  
Police Officers ◽  
Community Policing ◽  
Ordinary Least Squares ◽  
Future Research ◽  
Content Type ◽  
Order Maintenance ◽  
Role Orientations ◽  
Maintenance Activities

Purpose The purpose of this paper is to examine police officers’ attitudes toward community policing and order maintenance, as well as the facets of the work environment that impact those attitudes. Design/methodology/approach Survey data come from a sample of officers in a mid-sized police department. Ordinary least squares regression modeling is used to examine community-policing, order-maintenance and law-enforcement role orientations. Findings Officers endorse community partnerships, but are less enthusiastic about order maintenance. They also display mid-level support for traditional law enforcement. Work–environment variables have inconsistent impacts across the three role orientations. Research limitations/implications This was a survey of attitudes in one department. Future research should examine officers’ involvement in community-policing and order-maintenance activities and any impediments to such activities. Practical implications The findings have implications for police leaders seeking to implement community policing and ensure street-level officers are carrying out partnership and order-maintenance activities. In particular, top management must foster a positive work environment and personally model commitment to policing innovations. Originality/value This paper adds to the currently sparse body of literature on officer attitudes toward community policing and order maintenance, and incorporates traditional law-enforcement attitudes as a point of contrast. This paper advances the scholarly understanding of police officers’ role orientations.

scholarly journals Poverty and Prosperity: Impact on Livelihood Assets of Billion Trees Afforestation Program in Khyber Pakhtunkhwa (KPK), Pakistan

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 916
Author(s):  
Rauf ◽  
Khan ◽  
Shah ◽  
Zada ◽  
Malik ◽  
...  
Keyword(s):  
Ordinary Least Squares ◽  
Primary Data ◽  
Future Research ◽  
Positive Contribution ◽  
Sustainable Livelihood ◽  
Natural Forests ◽  
Khyber Pakhtunkhwa ◽  
Livelihood Assets ◽  
The Impact ◽  
Afforestation Program

In this study, we assessed the impact of the Billion Trees Afforestation Program (BTAP) on the livelihood of local household in Khyber Pakhtunkhwa Province (KPK). BTAP is the largest ban-logging afforestation program in Pakistan, which aims to conserve natural forests, promoting rural livelihoods and reducing poverty. Primary data from 360 local inhabitants were collected and analyzed using descriptive and econometric methodologies that include ordered logit model and ordinary least squares (OLS) respectively. In specific, a wealth index, household income, and five assets of sustainable livelihood have been considered to measure the impact of BTAP. We found that there is a strong and positive contribution of BTAP to the improvement of a rural community’s livelihood. Results showed that BTAP based households earn 4% more income and possess around 35% more assets. These findings suggest that BTAP has considerable effect on increase in livelihood assets. This study continues the discussion with several practical implications of this along with recommendations for future research.

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