Required Scripting and Work Stress in the Call Center Environment: A Preliminary Exploration
Bibliographic record
Abstract
INTRODUCTION In the past several decades, the growth of call centers has significantly impacted the work force in the United States. It is estimated that there are over 50,000 call centers operating in the U.S. alone, constituting 3% of the workforce (Batt, Doellgast, & Kwon, 2005). Call center sizes in the U.S. range from very small (with under 20) to large-scale operations (over 300). The average size of a call center in the U.S. is 289 employees (Batt, et al., 2005). Desai (2010) cites a report stating that all companies on the Fortune 500 have call centers and over $300 billion are spent on call centers annually. Globally, call centers are growing at a rate of 40% (Sprigg & Jackson, 2006). As one example, the number of Canadian call centers grew 27.7% from 2001 to 2006 (Echchakoui & Naji, 2013). With call centers representing such a significant portion of the work force, their impact on business operations is obviously significant (Batt, 2002). One reason for the widespread growth of call centers in the past several decades is technology. Technology, in general, has enabled companies to conduct business on a much larger scale, often globally. Services that were once provided through regional markets, have now become centralized global operations, with much of the work being completed via a call center (Batt & Moynihan, 2002). Call centers can be an effective tool for reaching the masses and when managed efficiently, they can become competitive advantages for companies. These centralization processes through the use call centers have allowed firms to create economies of scale by reducing offices, automating processes and simplifying its processes (Batt & Moynihan, 2002). With the vast growth of the call center industry and increased technology, companies have also increased the job expectations for Customer Service Representatives (CSRs). Jobs that were once known for simplified tasks such as collections or the handling of minor customer service issues now entail much more. In the U.S., 43% of call centers handle both service and sales functions (Batt, et al., 2005). This statistic implies that companies are utilizing their call centers for not only the traditional functions, such as customer service and collections (Bedics, Jack & McCary, 2006), but also as a source of revenue generation. Call centers are also being used as a channel for companies to engage in Customer Relationship Management (CRM) practices (Kantsperger & Kunz, 2005; Bedics, Jack & McCary, 2006) and the representatives are expected to build relationships with the patrons. Therefore, call centers are not only being used as service centers, but also as strategic pieces of business that are used to build revenue and build service relationships. These added expectations of the business have also led to added expectations of the service representatives who are completing the tasks. Traditionally, call center work has been characterized as low-skilled work, with its employees deemed as easily replaceable (Batt & Moynihan, 2002). Work within call centers has been compared to that of manufacturing, primarily because of the limited work discretion, task replication, and stringent work schedules (Hillmer, Hillmer, & McRoberts, 2004). These job characteristics have led call centers to be dubbed as white-collar production lines (Batt & Moynihan, 2002; Rose & Wright, 2005). This is primarily due to the technologies that are employed in call centers. The technologies allow business leaders to create a work environment for call centers that mirror assembly line work. The pace is controlled for the employee and there is limited job discretion (Varca, 2001). However, management prefers this work dynamic, as this provides efficiency to achieve those economies of scale that can add to profits. However, as companies move to generate revenue and use the centers as CRM tools, the job demands facing the front-line employees have elevated. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".