MétaCan
Menu
Back to cohort
Record W7019301158

Förbättrad operationell tid genom tjänster : En studie av kundvärdet och kundupplevelsen för tillgänglighetstjänster i lastbilsindustrin

2025· article· en· W7019301158 on OpenAlexaff

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsEngineering Link (Canada)
FundersLinköpings Universitet
KeywordsOriginal equipment manufacturerProduct (mathematics)Value propositionTruckThematic analysisService (business)Value (mathematics)Data collectionQuality (philosophy)
DOInot available

Abstract

fetched live from OpenAlex

The trucking industry is undergoing a significant transformation of business models, which is driven by technological advancements and increasing demands for operational efficiency. A central piece of this transition is the growth of availability services, which are service offerings that aim to maximize uptime and minimize unplanned stops for the customer. These services represent a shift to a more service-based business model through servitization and demand that truck manufacturers reconfigure how they create and capture value during the customer journey. As product and relationship quality become increasingly commodified, the customer experience of these services becomes the main differentiating component in value creation. Therefore, the purpose of this study is to develop a framework for how OEMs in the trucking industry can create and capture the value of availability services throughout the customer journey. The study was designed as a qualitative single case study, where the case company is a large European truck manufacturer. The data collection took place through semistructured interviews with case company employees and customers. Interviews were conducted with 17 customers from varying industries and with five respondents from the case company. Thematic data analysis was utilised with inspiration from the Gioia methodology. The findings of the study include a total of 16 value driving factors distributed into four dimensions of value in business markets. Namely, table stakes, functional value, ease of doing business value and individual value. Additionally, 16 activities that influence the customer experience of availability services were found and categorised into five groups. These groups are activities related to the purchase phase, daily interactions during the usage phase, moments of truth, background activities performed by the supplier and external activities performed by the customer. Lastly, a framework was created for how OEMs in the trucking industry can create and capture value of availability services throughout the customer journey by combining the identified value driving factors and activities. The conclusions showed that the different activities maintain different roles in creating and capturing value throughout the customer journey. Activities related to the purchase phase showcase the expected value for the customer and act as a door opener by selling a win-win solution that the customer accepts as economically beneficial. Phase spanning activities from the customer and the supplier ensure that the economical and individual value-in-use for the service offering is increased over time by creating a more coherent and contextually adapted experience. Daily interactions during the usage phase facilitate that the customer experience differentiates the supplier from its competitors. This is achieved by enabling and simplifying daily tasks within administration. Moments of truth are when the customer experience becomes accentuated. By utilising a quick and adaptive service organisation that uses transparent and factual communication, suppliers ensure that these instances take the customer experience to the next level.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.263
Teacher spread0.236 · 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 designNot applicable
Domainnot available
GenreEmpirical

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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueKTH Publication Database DiVA (KTH Royal Institute of Technology)Same topicService and Product InnovationFrench-language works237,207