Investigating essential factors of reseller perceived inequity and reseller performance in e-business
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
1. IntroductionWe live in the e-business era where virtually every business is engaged in some sort of electronic commerce to create value in terms of efficiencies or new business models. Electronic commerce sales have been growing steadily over year. According to the research firm Forrester, electronic commerce generated $262 billion in 2013, up from $231 Billion in 2012 (http://visual.ly/growth-ecommerce). As announced by the Census Bureau of the U.S. Department of Commerce, the retail electronic commerce sales for the first quarter of 2014 was $71.2 billion, with an increase of 2.8 percent (±0.7%) from the fourth quarter of 2013 (http://www.census.gov/retail/mrts/www/data/pdf/ec_current.pdf).In this e-business era, companies that want to be competitive should better be involved in business to business (B2B) electronic commerce [Standing and Lin 2007]. Among different types of businesses, business-to-business (B2B) market is considered as the largest one and has been growing rapidly in recent years. Compared with business-to-consumer (B2C) and consumer-to-consumer (C2C), B2B has a much larger market share [Schneider 2012]. As reported in 2011, 53% of the total of 5.93 million businesses in the United States were engaged in some kinds of B2B activities; in addition, US wholesalers generated over $5 trillion in sales and made $1.2 million in purchases (http://www.businessvibes.com/blog/size-b2b-market-united-states). B2B electronic commerce is practiced via different forms of systems that have become fundamental to business operations to many firms [Chi et al 2007]. Among various relationships of B2B electronic commerce, a large and essential form is the one between manufacturers and their resellers. In this study, we focus on examining this type of relationships.The rapid development and increased advancement in information technology has enabled modern companies to manage the manufacturer-reseller relationships more efficiently and effectively. As emphasized by researchers, interorganizational systems employing information technology may be the most important technological breakthrough in channels of distribution since air transport [O'Callaghan et al. 1992, p. 45]. Major manufacturers such as Caterpillar, Renault and HP are using Web-based Partner Relationship Management (PRM) software to manage and coordinate thousands of their resellers around the world. Cisco manages around 55,000 of its resellers and channel partners via PRM tools and derives about 80% of revenue through this indirect channel [Storey and Kocabasoglu-Hillmer 2013]. Such software is commonly referred to as e-business tools [Chakravorti et al 2015; Chen and Holsapple 2013; Lee et al. 2011; Mirani et al 2001], and is designed to automate the existing business processes such as ordering, technical support, communication, lead sharing and coordination of promotional activities [Liu et al 2015; Chen and Holsapple 2013; Theodosiou and Katsikea 2012]. While companies report increased efficiency as a result of using PRM tools [Chakravorti et al 2015], some Amazon's marketplace partners view the inherent transparency of online partnerships as detrimental to the 'junior' partners. They accuse Amazon of monitoring the partners' sales closely and competing directly and unfairly with their partners on the successful products in the marketplace [Wall Street Journal 2012].Among different processes involved in the manufacturer-reseller relationships, one of the most important processes is the enforcement process [Wu and Wu 2015; Heide 1994]. Previous research has identified two types of enforcement process, contractual enforcement [Johnson and Sohi 2015; Griffith and Zhao 2015; Gilliland, Bello and Gundlach 2010; Antia and Frazier 2001] and social, self-enforcement or norm-based enforcement [Wu and Wu 2015; Gilliland, Bello and Gundlach 2010; Heide 1994], both of which are considered as relationship management factors. …
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it