Personal LinkedIn account

Advertising on LinkedIn requires an active personal account associated with a real individual (i.e., a verifiable professional identity). During registration, the user must provide standard information such as name, location, job title, and current employer. For authenticity and compliance with LinkedIn’s advertising policies, the account should not be newly created: it must be at least 24 hours old, have at least one verified connection, and represent a verifiable professional identity. These criteria ensure that advertisements are traceable to legitimate users rather than anonymous entities or automated accounts. Importantly, these personal accounts are used for access and accountability within LinkedIn’s advertising system, but they are not displayed as the public-facing identity of the ads. Instead, the advertisements are shown as being placed by the LinkedIn Page (described below), meaning it is not externally visible which individual account initiated or manages a given campaign.

LinkedIn ad account

Once a personal profile exists, an advertising account can be established via the LinkedIn Advertise platform. Selecting “Create ad” or “Get Started” initiates the process (see Figure 1).

Figure 1: Starting page of LinkedIn Advertise website.

The platform then prompts the user to assign an account name and link the account to a LinkedIn Page (see Figure 2). If no appropriate institutional page is available, a new one must be created.

Figure 2: LinkedIn ads account set up.

After this step, the ad account provides access to campaign creation, audience definition, and budgeting functions.

LinkedIn page

A LinkedIn page functions as the institutional or organizational identity under which advertisements are displayed. Depending on the project’s context, the page may represent:

• a company or organization (e.g., a research institute),
• a showcase page (a subpage linked to a main institutional account), or
• an educational institution (e.g., an University).

Unlike platforms such as Facebook (Meta), LinkedIn does not currently offer a dedicated project or educational page type, meaning researchers must register under one of the existing categories. Each page requires basic information such as a name, a public URL, the industry, and the size of the organization (see Figure 3). In addition, it is possible to indicate further information (i.e., logo and a short organizational slogan). For research recruitment, using an institutional page (for example, a university department or research group) can increase credibility and transparency in the eyes of potential participants.

Figure 3: Set up LinkedIn page – example: educational institute.

Campaign level

At the highest level, the campaign establishes the overarching goal and organizational framework of the recruitment project. LinkedIn offers three types of objectives (see Figure 5):

• Awareness (increasing visibility),
• Consideration (encouraging engagement), and
• Conversion (prompting specific actions).

For survey recruitment, the “Website visits” option under the Consideration category is generally most suitable, as it directs users to an external landing page, typically the survey entry point.

Figure 5: Choosing a campaign objective

Once the campaign objective has been chosen, LinkedIn prompts the user to select a campaign type. Two options are available: a manual setup (“Classic”) and an AI-assisted setup (“Accelerate”) (see Figure 6). While the latter automatically adjusts parameters to optimize engagement, we strongly recommend avoiding it. Algorithmic optimization introduces unknown and uncontrolled sources of bias into audience selection, thereby comprising the transparency and replicability required in scientific research. Manual configuration ensures that all targeting and budgeting decisions remain under the advertiser’s, that is, the researcher’s control and can be documented in detail.

Figure 6: Selecting a campaign type

After selecting the campaign type, researchers must determine the ad creation process, that is, how individual advertisement will be assembled and optimized within the campaign. Again, LinkedIn offers two routes: Flexible ad creation and standard ad creation (see Figure 7). In the flexible ad creation mode, the platform automatically generates and tests different combinations of ad elements (e.g., images, headlines, and descriptions) and allocates the budget to those combinations that achieve the best predicted performance. This process resembles A/B testing but is fully automated. Although potentially efficient, this approach lacks transparency. The platform does not disclose the criteria used to define “best performance” nor how these predictions are generated. Consequently, it introduces algorithmic bias and variation that are not under research control. In research contexts, the standard ad creation mode is clearly preferable. This mode requires researchers to manually configure each ad creatively. All text and visual elements, as well as audience and delivery settings, are explicitly defined by the researchers. This manual process ensures that recruitment materials remain consistent and that all campaign parameters are fully reproducible and can be reported in a methods section.

Figure 7: Selecting an ad creation process

After determining the creation route, the campaign configuration also includes the definition of schedule and budget parameters (see Figure 8). Researchers may specify:

• a start and end date for the campaign,
• a total (lifetime) budget, and
• whether budget optimization across campaigns should be enabled.

Again, we recommend disabling all forms of automated budget optimization. Although these features can improve marketing efficiency by reallocating funds to higher-performing ads, they rely on algorithmic assessments that are not accessible to researchers and could bias the recruited sample in unknown and unobservable ways.

Figure 8: Campaign details

Ad set level

At ad set level, the researcher defines the target population, that is, the group of LinkedIn users who will be exposed to the survey advertisement. LinkedIn provides numerous targeting attributes derived from the members’ professional profiles, company pages, and inferred behavioral or interest signals (see Figure 9). Table 1 provides a comprehensive overview of the main attribute categories and typical options within each. Where known, information on how LinkedIn obtains or infers that information is indicated.

Figure 9: Definition of target audience

As seen in Table 1, researchers have various options for target parameters. Therefore, is important that targeting parameters must never be used to discriminate against individuals based on protected characteristics such as gender, age, or actual or perceived race or ethnicity. Researchers should also comply with platform-specific restrictions and regional regulations that limit the use of sensitive targeting criteria. The option “enable audience expansion” should be deactivated (see Figure 9). This algorithmic extension may deliver ads to users outside the intended target population and thus undermine the transparency and controllability of the sample frame. LinkedIn’s Campaign Manager also displays an estimated performance forecast, showing projected target audience size, impressions, clicks, and costs (see Figure 10). These values can assist in budget planning but are based on proprietary models and should be interpreted as approximate indicators only, not as precise predictions.

Figure 10: Forecasted results

Once the campaign objectives and audience have been defined, the next step is the selection of an appropriate advertisement format (see Figure 11). LinkedIn currently offers several formats that differ in visual layout, degree of interactivity, and expected engagement levels. The choice of format should align with the overall research design, recruitment objectives, and desired intensity of participation involvement. Available formats include, among other:

• Single image ads: static image with headline and text,
• Carousel ads: multiple scrollable image cards that allow the presentation of different aspects or topics within one advertisement, and
• Video ads: short clip (15-30 seconds) combining visuals and text to communicate information dynamically.

For most survey recruitment, these formats are generally the most suitable due to their high visibility and clear message presentation.

Figure 11: Ad format

The integration of URL tracking parameters (e.g., UTM codes) into the destination link is recommended to enable transparent performance monitoring (see Figure 12). A typical example would be:

utm_source=[SOCIAL MEDIA PLATFORM]&utm_medium=[PAYMENT EVENT]&utm_campaign=[CAMPAIGN NAME]&utm_content=[AD ID].

This method complies with data protection requirements and does not collect personal information. It simply allows researchers to distinguish traffic from different platforms or creatives in their web analytics.

Figure 12: URL tracking parameters

Ad placement is then specified (see Figure 13). The LinkedIn feed should be selected as the primary placement to ensure visibility in a professional context. The LinkedIn Audience Network, which extends ad delivery to partner websites and apps, should be deactivated in research campaigns to maintain control and transparency regarding where exactly recruitment occurs.

Figure 13: Placements option

The budgeting model determines how funds are distributed over time (Figure 14). LinkedIn provides three options:

• Daily budget: specifies maximum daily expenditure, ensuring consistent ad delivery,
• Lifetime budget: sets a total expenditure limit for the campaign period, and
• Combined budget: applies both daily and lifetime limits for maximum control.

Short-term campaigns benefit from daily budgets to maintain stable exposure, while longer recruitment periods may require lifetime or combined budget settings. Key performance metrics such as impressions, click-through rate (CTR), and cost per click (CPC) should be monitored throughout the campaign and reported alongside sample characteristics.

Figure 14: Budget and schedule options

The bidding strategy determines how LinkedIn allocates ads in its auctions system (Figure 15). LinkedIn currently offers three bid types:

• Maximum delivery: an automated bidding option where LinkedIn’s system sets the bid with the goal of spending the full budget and maximizing the selected key result,
• Cost cap: an automated option where LinkedIn sets bids while trying to keep the average cost per key result close to a user-specified target (e.g., CPC or CPM), and
• Manual bidding: a more hands-on option where advertisers set an explicit bid value that is used in the ad auction.

From a research perspective, there is a trade-off between cost-efficiency and methodological control. Fully automated options such as maximum delivery can be convenient and may help use the budget efficiently, but they also shift additional decision-making to opaque algorithms. Where fine-grained cost control and transparency are priorities, cost cap or even manual bidding may be preferable, provided that campaigns are monitored closely, and bid levels are adjusted if necessary. Regardless of the chosen strategy, researchers should document the bid type and any changes during fieldwork so that recruitment conditions can be accurately reported.

Figure 15: Bidding options

Although LinkedIn supports conversion tracking via tracking pixels (see Figure 16), this method is ethically and legally problematic in research contexts. Tracking pixels typically collect behavioral data across websites (e.g., page views, time spent, subsequent actions) and can be used to link on-platform interactions with off-platform behavior. Under data protection frameworks such as the General Data Protection Regulation (GDPR), such tracking requires a valid legal basis and, in most cases, informed consent: participants must be clearly informed before any tracking takes place about what data are collected, for what purpose, on which legal basis, how long they are stored, and with whom they may be shared. Only if users have actively agreed (e.g., via a consent banner or consent form on the landing page) may tracking pixels be activated. However, for scientific campaigns, this form of tracking is usually difficult to justify. Social media platforms and advertising networks have repeatedly been affected by data breaches and other security incidents (e.g., Das 2023; Feathers et al. 2022; Fondrie-Teitler et al. 2022). When third-party tracking is enabled, additional actors gain access to behavioral data and identifiers, increasing the potential attack surface. Even if the probability of a breach is difficult to quantify, the possible consequences for participants – for example, the unintended disclosure of sensitive behavioral patterns or re-identification risks when log data are leaked – can be substantial. From a risk-minimization perspective, avoiding non-essential tracking is therefore a central protective measure. We therefore recommend that researchers do not use LinkedIn’s conversion tracking or similar third-party tracking pixels for recruitment. Instead, evaluation should rely on aggregate campaign statistics (impressions, clicks, CPC) provided by the platform, combined with survey-based indicators (e.g., completion rates and a self-reported recruitment channel question on the first page of the questionnaire, asking participants how they became aware of the study).

Figure 16: Conversion tracking

Ad level

At the ad level, the creative elements that LinkedIn users are exposed to are defined. The design of these elements can draw on established findings from research on invitation letters in survey methodology. In line with this literature, ad content should be convincing and informative, and should convey a clear perceived leverage for participation, for example by highlighting the social value of the study, the relevance of the topic for the target population, or the opportunity to contribute one’s perspective (e.g., Dillman et al. 2014 ; Groves et al. 2000). On LinkedIn, as on other social media platforms, an ad competes with a large volume of other digital content. Consequently, the ad must first attract attention within a very short time frame and then provide sufficient relevance and clarity to motivate users to click on the ad and proceed to the survey landing page. The introductory text (Figure 17) – typically the most prominent text element in the ad – should succinctly communicate the purpose of the study and its relevance for the targeted audience. A concise structure is advisable: one sentence specifying what the study is about and who it addresses, followed by a sentence that conveys why participation is meaningful (e.g., expected benefits, contribution to knowledge, or policy relevance).

Figure 17: Ad design - Text and media input

The visual content serves as the primary attention grabber and can function as an additional targeting device (Figure 17). On LinkedIn, visuals are often embedded in a professional and organizational context, for example by depicting typical work situations (e.g., team meetings, hybrid collaboration, industry-specific workplaces), abstract graphics related to the topic (e.g., charts, icons for digitalization or skills), or neutral employer-branding style imagery. When these visuals are clearly aligned with a specific survey topic – for instance, leadership and management practices, remote work arrangements, skills development, or employee well-being – they can increase click-through rates and the number of completed questionnaires, because they immediately signal topical relevance to certain occupational groups or sectors. At the same time, these visuals may amplify self-selection on work-related attitudes or experiences (e.g., job satisfaction, perceived stress, views on organizational change), as they are particularly salient for individuals with above-average involvement or strong opinions (Donzowa et al. 2025; Zindel et al., 2025). This implies a trade-off: if recruitment within a narrowly defined topic area is prioritized, topic-specific professional visuals are advantageous; if concerns about attitudinal bias are central, more neutral or general workplace imagery may be preferable. In addition to the visual itself, LinkedIn offers the option to set an image alt text (Figure 18). This alt text enhances accessibility for users who rely on screen readers and is displayed if the image cannot be rendered. From a survey recruitment perspective, the alt text should provide a concise, neutral description of what is shown in the image and, where appropriate, can briefly restate the topic of the study (e.g., “Illustration of professionals discussing climate policies in an office setting”). Overly promotional language or keyword stuffing should be avoided. The goal is to ensure that the meaning of the visual remains understandable even in the absence of the actual image and that the alt text is consistent with the overall framing of the study (cf. [World Wide Web Consortium [W3C], 2025[(https://www.w3.org/TR/WCAG21/)]).

Figure 18: Ad design - Further options

The destination URL is the link to the survey website and leads directly to the survey entry page, without intermediary pages or redirects. This entry page must present the informed consent statement and essential information on data protection, study sponsors, and contact details before data collection begins. From a user-experience perspective, the number of steps between the ad (i.e., the survey invitation) and the first survey question should be minimized, as each additional step potentially increases drop-off probabilities (cf. Tourangeau et al. 2013). Besides the introductory text and the image, LinkedIn ads typically include a headline and an optional ad description. The headline is usually displayed in a highly prominent position and should therefore be short, specific, and benefit oriented. For survey recruitment, headlines that clearly state the topic and target group (e.g., “Short survey on remote work for HR professionals”) are generally preferable to vague or purely promotional formulations. The ad description provides additional information to people who see the ad. However, this field is not visible in most scenarios and will only appear for a small portion of LinkedIn members, for example in certain placements on the LinkedIn Audience Network (if enabled). Accordingly, essential information about the study should not be placed exclusively in the description but should already be contained in the introductory text and headline. The description can be used as a secondary field to elaborate on the headline, clarify what happens after the click, indicate approximate survey length, or highlight a specific leverage (e.g., “10-minute academic survey, anonymous, results will inform future workplace policies”). Given that descriptions may be truncated or omitted depending on device and placement, key information should appear at the beginning of the text and should not be critical for understanding the study. The call-to-action (CTA) button should be consistent with the overall framing of the ad and align with user expectations on a professional platform. LinkedIn offers several standard CTA options, including “Learn more”, “Sign up”, “Register”, and others. For survey recruitment, a CTA such as “Learn more” is typically most appropriate, as it signals low-threshold, information-oriented action compatible with participation in a research study. In settings where the survey is embedded in a broader program or panel, “Sign up” or “Register” may also be suitable.

Figure 19: Ad preview

Prior to launch, the preview function in Campaign Manager should be used to verify appearance across devices and placements (see Figure 19). Across all ad components, internal consistency is crucial. The introductory text, headline, description, visual elements (including alt text), destination URL, and CTA should convey a coherent message and set realistic expectations about the survey experience. A consistent, methodologically informed ad design that is at the same time adapted to platform-specific norms on LinkedIn potentially increases the likelihood of attracting suitable respondents rather than merely generating clicks.

Ad creatives

Two separate advertisements in the single image format were developed. They used identical textual content but different ad images to examine how visuals influence engagement (Figures 21 and 22).

Headline: We want your opinion: Take part in our survey on the German labor market! Introductory text: Help us better understand the German labor market! We are looking for participants for a short survey that is part of a scientific study conducted by Bielefeld University. The survey is aimed at all employed people aged 18 and over in Germany.

Figure 21: Targeting ad 1 used to recruit participants on LinkedIn

Figure 22: Targeting ad 2 used to recruit participants on LinkedIn

Campaign setup

Both advertisements employed identical targeting criteria to ensure comparability across creatives. To recruit broadly from employed individuals in Germany, targeting was restricted to:

• Location: Germany
• Profile Language: German
• Age: 18+ years

No additional targeting criteria were applied, and audience expansion was disabled. The placement was set to LinkedIn Feed only and audience network placements was disabled. A lifetime budget of 500 EUR was defined, optimization goal was landing page clicks, and the bidding strategy was maximum delivery until all the budget is spent.