Supporting business processes through human and IT factors: a maturity model
Bibliographic record
Abstract
Purpose The purpose of this paper is to assist organizations in the assessment of both information technology (IT) and human factors required to support their business processes (BPs) by taking into account the interdependence and alignment of these factors, rather than considering them independently. Design/methodology/approach A design science research methodology was followed to build a maturity model (MM) enabling this assessment. The proposed design process is composed of four steps: problem identification, comparison of 19 existing MMs in business process management (BPM), iterative model development, and model evaluation. The last two steps were specifically based on three research methods: literature analysis, case studies, and expert panels. Findings This paper presents a MM that assigns a maturity level to an organization’s BPs in two assessment steps. The first step evaluates the level of sophistication and integration of the IT systems supporting each BP, while the second step assesses the alignment of human factors with the technological efforts. Research limitations/implications The research was conducted with SMEs, leading to results that may be specific to this type of organization. Practical implications Practitioners can use the proposed model throughout their journey toward process excellence. The application of this model leads to two main process improvement scenarios: upgrading the sophistication and integration of the software technologies in support of the processes, and improving the cohesion of the resources the organization already owns (human and IT resources). Originality/value The proposed MM constitutes a first step in the assessment of the interdependence between the factors influencing BPM.
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 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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".