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DIJITALPI / RESEARCH

How can artificial intelligence
create value for your organisation?

Can customers find you through AI? Does your assistant provide accurate answers? We use published research and our own measurements to investigate these questions.

The research question, method, findings, limitations and practical meaning.

Transparent research status

We disclose whether a page reviews published work or reports an ongoing DijitalPi study.

WHAT YOU WILL FIND HERE

AI research,
explained clearly.

DijitalPi Research reviews academic studies and explains what they may mean for organisations through concrete examples. Topics include AI advertising, productivity, decision quality, citations, culture and visibility in generative answers.

Every review identifies the researchers, original source, method, findings and open questions. We label DijitalPi's interpretation and examples separately. We also share the progress and limitations of studies we conduct ourselves.

Explore the research articles

01 / DIJITALPI ORIGINAL RESEARCH

Our own measurement
AI VISIBILITY · TURKEY

Which agencies does AI recommend
when customers ask for help?

We are preparing repeated measurements on ChatGPT, Gemini and Claude using questions a customer may ask. We record which agencies are mentioned, explicitly recommended and cited.

Why it matters: understand which brands and sources appear along a potential customer's discovery path.

See how we conduct the study

Data collection and evaluation continue. No final result has been published.

02 / ACADEMIC PAPER REVIEWS

Türkiye + international

What can these findings
do for my organisation?

We introduce each original study, explain its findings and limitations, and discuss how a team might apply the lesson without turning it into a claim the source did not make.

01 / AUDIENCE BALANCE IN ADVERTISING TESTS

Can I trust the winning ad in a Meta A/B test?

Preprint review · Source language: English

Paper reviewed: Characterizing and Minimizing Divergent Delivery in Meta Advertising Experiments
arXiv · 28 August 2025 · v1, peer review not documented · Preprint

Does a winning Meta ad reveal a stronger message or a different audience? This preprint examines what A/B results can support in creative decisions.

Editorial review draft · Illustrative marketing examples; no client experiment
Read the review
02 / MEASURING INCREMENTAL ADVERTISING EFFECTS

Can last-click data mislead your advertising budget decisions?

Preprint review · Source language: English

Paper reviewed: Predicted Incrementality by Experimentation (PIE) for Ad Measurement
arXiv · 1 April 2026 · v2 · Preprint

Can last-click data support budget decisions? A preprint compares experiment-trained predictions with attribution when judging campaign success.

Editorial review draft · Illustrative marketing examples; no client experiment
Read the review
03 / AI AND PURCHASING BEHAVIOUR

Does a smarter AI assistant increase sales?

Working paper review · Source language: English

Paper reviewed: Reasoning AI, Consumer Search, and Purchase: Evidence from an Online Platform Field Experiment
SSRN · 23 May 2026 · Examined PDF version, not peer reviewed · Working paper

Does a smarter assistant increase sales? We examine Ctrip’s booking decline and implications for marketing measurement in a non-peer-reviewed working paper.

Editorial review draft · Illustrative marketing examples; no client experiment
Read the review
04 / AI ANSWERS IN SEARCH

Do AI answers in on-site search increase orders?

Peer-reviewed article review · Source language: English

Paper reviewed: Generative Search: Evidence from a Large-Scale Field Experiment
Management Science / SSRN · 13 May 2026 · Reviewed SSRN author manuscript; published online in the journal 24 September 2026 · Peer-reviewed article

Can AI answers in on-site search increase orders? A peer-reviewed article reports small gains in a Chinese field experiment, with limits for marketing decisions.

Editorial review draft · Illustrative marketing examples; no client experiment
Read the review
05 / CHATGPT TRAFFIC AND SALES

Do visitors from ChatGPT buy more often?

Peer-reviewed article review · Source language: English

Paper reviewed: Frontiers: ChatGPT Referrals to E-Commerce Websites: How Do LLMs Compare Against Traditional Channels?
Marketing Science · 21 April 2026 · Journal version, online publication · Peer-reviewed article

Do visitors from ChatGPT buy more often? A peer-reviewed article compares channel performance and informs realistic e-commerce budget decisions.

Editorial review draft · Illustrative marketing examples; no client experiment
Read the review
06 / AI SEARCH AND WEBSITE TRAFFIC

Does Google AI Mode reduce clicks to websites?

Preprint review · Source language: English

Paper reviewed: AI in Search Reduces Publisher Referrals Without Improving User Experience: Experimental Evidence
arXiv · 18 August 2026 · v1 · Preprint

What does a US Google field experiment reveal about external clicks? We examine the findings, marketing implications and limits of applying them in Türkiye.

Editorial review draft · Illustrative marketing examples; no client experiment
Read the review
07 / FROM AI RECOMMENDATIONS TO BRAND VISITS

Do AI brand recommendations bring visitors?

Preprint review · Source language: English

Paper reviewed: From Prompt to Purchase: How AI Brand Recommendations Move Consumers on the Open Web
arXiv · v1: 9 June 2026 · Reviewed v2: 31 August 2026 · Preprint

We examine the relationship between AI brand recommendations and later searches and visits, keeping visibility, website traffic and sales distinct.

Editorial review draft · Illustrative marketing examples; no client experiment
Read the review
08 / STAGES OF BRAND VISIBILITY IN AI SEARCH

Where do brands drop out of AI search answers?

Preprint review · Source language: English

Paper reviewed: From Prompt to Recommendation: A Fitted Stage Model of Brand Visibility in AI Search
arXiv · 19 September 2026 · v1 · Preprint · Commercial Aiso data

Why distinguish content fit, citations and brand mentions? We examine Aiso’s stage model, its evidence and the limits of commercial monitoring data.

Editorial review draft · Illustrative marketing examples; no client experiment
Read the review
09 / AI ADVERTISING PRETESTS

Can you rehearse an advertisement with AI before filming?

Peer-reviewed paper review · Source language: English

Paper reviewed: Pretesting with AI-generated static and video ads
Scientific Reports · 15 August 2026 · Peer-reviewed, accepted Article in Press; awaiting editorial corrections

Which decisions can AI advertising rehearsals support? We examine peer-reviewed evidence, quality screening, measurement limits and illustrative business applications.

Editorial review draft · Illustrative marketing examples; no client experiment
Read the review
10 / BRAND IMAGE IN AI ADVERTISING

Does a polished AI advertisement also build trust?

Research article review · Source language: Turkish

Paper reviewed: Yapay Zekâ ile Üretilen Reklam Filmlerinde Marka İmajı
Adıyaman Üniversitesi Sosyal Bilimler Enstitüsü Dergisi · 12 August 2026 · 53:342–376

A Turkish-language study helps distinguish visual quality, authenticity and brand trust, with three practical marketing examples.

Turkish source · Editorial review draft · Business examples are DijitalPi commentary
Read the review
11 / AUDIENCE REACTIONS TO AI ADVERTISING

Are more comments on an AI advertisement good news?

Research article review · Source language: Turkish

Paper reviewed: Yapay Zekâ ile Üretilen Reklamlara Yönelik Tüketici Tepkileri: Coca-Cola “Holidays Are Coming” Reklamının Duygu Analizi
Kronotop İletişim Dergisi · 29 July 2026 · 3(2):407–433

A Turkish-language study of a Coca-Cola advertisement helps separate comment volume, sentiment and sales outcomes.

Turkish source · Editorial review draft · Business examples are DijitalPi commentary
Read the review
12 / CUSTOMER ACCEPTANCE OF CHATBOTS

Why would your customer use a chatbot?

Research article review · Source language: Turkish

Paper reviewed: Teknoloji Kabul Modeli Kapsamında Sohbet Robotlarına Yönelik Tüketici Kabulü Üzerine Bir Araştırma
Pazarlama ve Pazarlama Araştırmaları Dergisi · 20 May 2026 · 19(2):370–397

A Turkish-language consumer study connects chatbot usefulness, ease and trust to practical marketing questions.

Turkish source · Editorial review draft · Business examples are DijitalPi commentary
Read the review
13 / IMPARTIAL AI SHOPPING ADVICE

Whose interests does an AI shopping assistant serve?

Preprint review · Source language: English

Paper reviewed: Whom Do AI Agents Work For? Role Assignment Induces Sponsorship Bias in LLM Recommenders
arXiv · 16 September 2026 · v1 · Preprint

How should marketers assess shopping recommendations? We discuss a new study through practical examples of product fit, sponsorship and sales advice.

Editorial review draft · Illustrative marketing examples; no client experiment
Read the review
14 / AI AND CUSTOMER RESEARCH

Can AI answer on behalf of your customers?

Preprint review · Source language: English

Paper reviewed: Synthetic Data in Marketing Research: How to Evaluate and When to Trust
arXiv · 12 September 2026 · v1 · Preprint

Can simulated customers guide a campaign decision? We discuss synthetic-data research through message testing and real-customer validation examples.

Editorial review draft · Illustrative marketing examples; no client experiment
Read the review
15 / BUSINESS IMPACT OF AI VISIBILITY

Can we measure the sales impact of AI visibility?

Preprint review · Source language: English

Paper reviewed: Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact
arXiv · 10 September 2026 · v1 · Method and simulation preprint

What does an AI mention mean for your business? We discuss a new measurement framework through marketing reports and budget decisions.

Editorial review draft · Illustrative marketing examples; no client experiment
Read the review
16 / WORKING WITH AI IN MARKETING TEAMS

How should your marketing team work with AI?

Peer-reviewed paper review · Source language: English

Paper reviewed: Dancing with AI: how human-AI interaction affects employee task performance
Humanities and Social Sciences Communications · 9 September 2026 · 13:1578 · Peer-reviewed; version of record 17 September 2026

Who produces, checks and approves AI-assisted work? We discuss a new workplace study through content, advertising and customer communication examples.

Editorial review draft · Illustrative marketing examples; no client experiment
Read the review
17 / AI VIDEO ADVERTISING

Do personalized AI video ads increase engagement?

Peer-reviewed article review · Source language: English

Paper reviewed: Frontiers: Generative AI and Personalized Video Advertisements
Marketing Science · 8 May 2025 · 44(4):733–747 · Peer-reviewed field experiment

A real WhatsApp advertising experiment compared AI video, personalized images and generic video. We explain what the six-to-nine-point engagement difference does and does not show.

Published English review · Engagement is not a sales outcome
Read the review
18 / AI PRODUCTIVITY IN CUSTOMER SERVICE

Does AI really make customer-service agents more productive?

Peer-reviewed article review · Source language: English

Paper reviewed: Generative AI at Work
The Quarterly Journal of Economics · 4 February 2025 · 140(2):889–942 · Peer-reviewed article

Data from 5,172 agents and three million chats showed a 15% average rise in issues resolved per hour with AI assistance. We examine why less-experienced workers gained most.

Published English review · Limited to one company and task setting
Read the review
19 / AI AND TEAMWORK

Can AI replace a teammate?

Peer-reviewed article review · Source language: English

Paper reviewed: The Cybernetic Teammate: A Field Experiment on Generative AI and Teamwork
Organization Science · 12 June 2026 · 37(4):1217–1242 · Peer-reviewed field experiment

A field experiment with 791 P&G professionals found that AI-assisted individuals approached two-person teams on some product-innovation tasks. We explain where human selection retained value.

Published English review · One-day innovation workshop
Read the review
20 / HUMAN AND AI CREATIVITY

Are humans or AI more creative?

Peer-reviewed article review · Source language: English

Paper reviewed: A large-scale comparison of divergent creativity in humans and large language models
Nature Human Behaviour · 23 December 2025 · 10:531–540 (2026) · Peer-reviewed article

Across 9,198 people and 215,542 model observations, humans held a small average lead and a larger advantage at the creative extreme. We examine why “think like a genius” prompts were not enough.

Published English review · One established divergent-creativity task
Read the review
21 / AI ASSISTANCE ON SOCIAL MEDIA

Can AI increase participation while reducing social-media quality?

Peer-reviewed article review · Source language: English

Paper reviewed: The impact of generative AI on social media: an experimental study
Scientific Reports · 17 February 2026 · 16:9376 · Peer-reviewed experimental article

In a realistic social-media experiment with 680 people, some AI tools increased participation while reducing perceived quality and authenticity. We examine spillovers to other participants.

Published English review · Controlled platform experiment
Read the review
22 / THE FRUIT-FLY BRAIN CONNECTOME

How scientists mapped a fruit-fly brain

Peer-reviewed article review · Source language: English

Paper reviewed: Neuronal wiring diagram of an adult brain
Nature · 3 October 2024 · 634:124–138 · Peer-reviewed research

Researchers mapped 139,255 neurons and 54.5 million synapses in one adult fruit fly at connection level. We explain what this map reveals and what it still cannot tell us.

Published English review · A wiring diagram is not a complete record of brain activity
Read the review
23 / HIRING AN AI EMPLOYEE

Is an AI employee really ready for work?

preprint review · Source language: English

Paper reviewed: READY or Not: Reliable Enterprise Agent Deployment
arXiv · September 2, 2026 · v1 · Preprint

An AI system can complete the task, but how much review will your team still need? Consider accuracy, review effort and total cost together when choosing an AI solution.

English review draft · Peer-reviewed publication not verified
Read the review
24 / AI DISCLOSURE IN ADVERTISING

How do customers respond when an ad discloses AI use?

peer-reviewed article review · Source language: English

Paper reviewed: “Made with AI” but why? How consumers interpret beneficiary-framed AI disclosures in advertising
Journal of Marketing Analytics · 27 August 2026 · Peer-reviewed article

What difference does it make to write “Created with AI” in the ad and explain why? We explain the methodology of the three experiments, their different results, and their meaning for the brand team.

English review draft · No own campaign testing done
Read the review
25 / AI AGREEMENT AND DECISION QUALITY

When AI agrees with you, does that mean you are right?

preprint review · Source language: English

Paper reviewed: Individual-level interventions against sycophantic AI reduce its appeal but not its persuasiveness
arXiv · First release: July 28, 2026 · Reviewed v3: August 2, 2026 · Preprint

Is the assistant's support for your idea an independent assessment? We describe the findings of two experiments, the impact of warnings, and what they mean when making decisions.

English review draft · Peer-reviewed publication not verified
Read the review
26 / PERSONALIZED AI PERSUASION

Is personalized AI more persuasive than humans?

peer-reviewed article review · Source language: English

Paper reviewed: On the conversational persuasiveness of GPT-4
Nature Human Behavior · 19 May 2025 · 9:1645–1653 · Peer-reviewed article

How convincing was GPT-4 when it accessed basic personal information in brief online discussions? We describe the robust finding of the 900-participant experiment and its limitations that cannot be directly generalized to marketing.

Published English review · Not a marketing campaign test
Read the review
27 / THE PERSUASIVE POWER OF AI MESSAGES

Are AI messages as persuasive as human messages?

peer-reviewed article review · Source language: English

Paper reviewed: LLM-generated messages can persuade humans on policy issues
Nature Communications · 1 July 2025 · 16:6037 · Peer-reviewed article

In three preregistered experiments, 4,829 people read policy messages written by AI and ordinary people. We explain the small shifts in attitude, why the messages were perceived differently and what remains unknown for marketing.

Published English review · Political attitude experiment is not sales research
Read the review
28 / AI, LANGUAGE AND CULTURE

Does AI think differently depending on the language you use?

peer-reviewed article review · Source language: English

Paper reviewed: Cultural tendencies in generative AI
Nature Human Behavior · 20 June 2025 · 9:2360–2369 · Peer-reviewed article

GPT and ERNIE showed different cultural tendencies when responding to the same measures in English and Chinese. We examine the meaning of language choice for localization, advertising ideation and international brand communication.

Published English review · English and Chinese model outputs reviewed
Read the review
29 / AI AND SALES IN E-COMMERCE

Does generative AI really increase e-commerce sales?

preprint review · Source language: English

Paper reviewed: Generative AI and Sales Productivity: Field Experiments in Online Retail
arXiv · First release: October 14, 2025 · Revised v6: June 29, 2026 · Preprint; the authors state that it has been accepted by Management Science

In seven field experiments spanning millions of users and products, AI's sales impact varied by workflow. We explain where we're seeing the biggest growth, what's missing from ad headlines, and how businesses can set up a pilot.

Published English review · Field experiments on a single e-commerce platform
Read the review
30 / TRUST IN HEALTH COMMUNICATION

Why do we trust health content in the age of AI?

Academic paper review · Source language: English

Paper reviewed: Multilayered Epistemic Disruption in AI-Driven Health Misinformation: Conceptual Framework and Viewpoint
JMIR Infodemiology · 20 August 2026 · 6:e96664 · Viewpoint and conceptual framework

The MEDF research question, its four-layer framework and its proposed approach to trust in health communication.

English review draft · Conceptual framework not yet empirically validated
Read the review
31 / VISIBILITY IN AI

Does greater visibility in AI bring more customers?

Academic paper review · Source language: English

Paper reviewed: GEO: Generative Engine Optimization
KDD 2024 · conference paper

A "40% increase" in a study does not mean that sales rose by 40%. We explain which results to examine when deciding whether investment in your website is paying off.

English review draft · Our own application test has not yet been conducted
Read the review
32 / GIVING CUSTOMERS ACCURATE ANSWERS

Can AI give your customers incorrect information?

Academic paper review · Source language: Turkish

Paper reviewed: Türkçe soru cevaplama için büyük dil modelleri üzerinde geniş ölçekli etki analizi
Journal of Gazi University Faculty of Engineering and Architecture · 2025

An assistant may quote the correct price but misrepresent the return policy. We explain with examples which questions you should try before introducing the assistant to your customers.

30 mock answers available · Answers have not yet been human-rated
Read the review
33 / CHECKING ANSWER ACCURACY

Can we trust AI when it provides citations?

Academic paper review · Source language: English

Paper reviewed: Enabling Large Language Models to Generate Text with Citations
EMNLP 2023 · conference paper

How did ALCE researchers evaluate the sourced answers and what did they find? We explain the meaning of the findings and compare the claim with its basis in four examples.

English review draft · Includes four teaching examples
Read the review

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PUBLICATION APPROACH

Clear explanation.
Verifiable sources.

  1. State the question and who was studied.
  2. Explain when and how the work was conducted.
  3. Do not present unknowns as results.
  4. Link the sources and correct errors.

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