MétaCan
Menu
Back to cohort

Supporting E-Commerce Strategy through Web Initiatives

2009· book-chapter· en· W2781445152 on OpenAlexaff
Ron Craig

Bibliographic record

VenueIGI Global eBooks · 2009
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicInformation Technology Governance and Strategy
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsPaceThe InternetBusinessBoomWorld Wide WebWeb engineeringWeb developmentEngineeringComputer scienceWeb intelligence

Abstract

fetched live from OpenAlex

Our understanding of “the Web” and its e-commerce (EC) potential has grown rapidly during the past decade. While ecommerce has matured and is now mainstream, there continue to be opportunities to innovate as technology improves, the public is increasingly comfortable with and dependent up the e-approach, and new or enhanced applications appear. While historical roots of the Web go back several decades, it was only in the last two that business really started to embrace the Internet, and in the last one that commercial opportunities on the Web grew rapidly. Business use has gone from simple operational efficiencies (e-mail on the Internet, replacement of private EDI networks, etc.) to effectiveness (enhanced services, virtual products, and competitive advantage). Information and information products, available in digital form, and the ability to quickly transfer these from one party to another, have led to a paradigm shift in the way organizations operate. Many BPR (business process re-engineering) projects made use of the Web to streamline business processes and reduce or eliminate delays. Web self-service has emerged as a popular approach, with benefits for both customers and providers. Even governments have embraced the Web (e-government) for information and service delivery and interaction with citizens and businesses. While the transition has followed the historical IT progression of automate, infomate, and transformate, the pace has been unprecedented. There have been successes and failures, with fortunes made and lost. After the dot-com boom/bust cycle, things settled down somewhat; yet the rapid pace of Web initiatives continues. At the forefront are innovators seeking competitive advantage. At the rear are laggards who can no longer ignore efficiencies provided by the Web and market requirements to be Web-enabled. Paralleling the improvement in IT and the Internet has been a series of economic shifts including globalization, flattening of hierarchical organizations, outsourcing and off-shoring, increasing emphasis on knowledge work (contrasted with manual labor), plus growth in the service sector and information economy. IT has both hastened these economic shifts and provided a welcome means of addressing the accompanying pressures (often through EC or other Web initiatives). To consider EC strategy and Web initiatives, one first needs to understand strategy and then extend this to the organization’s business model and tactics. A firm’s general business strategy includes, but is not limited to, its IT strategy (Figure 1). Similarly, EC strategy is a subset of IT strategy. Strategy should drive actions (tactics), through an appropriate business model. When strategy (business, IT, and EC) and tactics are closely aligned, and tactics are successfully executed, desirable results are obtained. Sometimes this normative view becomes reversed or otherwise changed. In the extreme, Web initiatives become the sole major focus (as was the case in the early days of the dot-com boom). However, without alignment between such tactics and the firm’s strategy and business model, such an approach is either doomed to eventual failure or substantial modification. In addition to commercial use of the Web, there are many non-commercial uses and non-commercial users (governments, educational institutions, medical organizations, etc.). The term e-business is often used to include both commercial and non-commercial activity on the Internet. In this article, the focus is on commercial activities (B2B and B2C). While e-government includes use of EC, governments are often driven by goals and responsibilities other than profit generation or cost reduction.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.266
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueIGI Global eBooksSame topicInformation Technology Governance and StrategyFrench-language works237,207