Optimizing short video strategies for intelligent communication in university campus culture construction using circular intuitionistic fuzzy COCOFISO modeling

Abstract

Short-video communication is becoming a more common tool in universities to influence campus culture, but the choice of strategies and tools is not a straightforward one due to the variety of goals at particular institutions, limited resources, and the lack of reliability in expert advice. The paper proposes a circular intuitionistic fuzzy combined compromise solution to the ideal solution optimization (C-IF COCOFISO) method for ranking candidate short-video strategies based on a number of possibly conflicting criteria. Modelling the expert judgments as circular intuitionistic fuzzy constructions, which are combined and their aggregation is performed in a manner incorporating a combined compromise solution to ideal solution optimization (COCOFISO) procedure, led to consistently stable and easily interpretable priority scores. The criteria include the suitability of cultural relevance, effectiveness in communication, flexibility in technology, opportunities for student engagement, ethics, and sustainability. The framework consolidates fuzzy expressed fuzzy modelling and a clear-cut compromise-driven ranking pipeline, enabling decision-makers to handle waffling and trade-offs in one process. In particular, it combines C-IF data with COCOFISO to overcome competing goals and incomplete decisions, offers a step-by-step procedure that produces reproducible weights and rankings that are appropriate to institutional planning, and is tested on an actual university case, including sensitivity and cross-method comparisons. The findings show that C-IF COCOFISO provides a similar ranking and is more sensitive to cultural goals than the minimal requirements of decision-making processes, making it a flexible and convenient tool for drawing short-video communication plans in higher education.

Introduction

Short video platforms have proved to be an essential tool of university culture creation and enrichment, transmission of institutional values, provoking the creativity of students, and developing informed communication. However, the successful implementations of short-video tactics are loosely structured by issues of content quality management, security of data, technological flexibility, affordability, and reachability. The dynamic nature of the digital communication technologies and the budgetary limitations of the institutions, not to mention ethical considerations, complicate the process of decision-making under these circumstances and are all phenomena that should be examined systematically¹. Fuzzy set (FS) theory, which was proposed by Zadeh², provides a conceptual framework through which one can have flexible reasoning incorporating various decision-making degrees (MDs). Atanassov³ later, expanded the work of Zadeh by introducing interval-valued FSs to interconnect MDs and NMDs non-membership degrees, and this increased the ability to deal with uncertainty. Subsequently, Pythagorean FSs⁴ followed, as well as q-rung orthopair FSs⁵, adding more decision modelling tools to the complex decision environment. Cuong⁶ generalized these forms by incorporating degrees of abstinence (ADs), which makes them specifically appropriate in assessing digital initiatives in a setting of uncertainty.

Although Circular Intuitionistic Fuzzy Sets (C-IFS) are increasingly utilized in sophisticated applications of MCDM, the scientific coverage of these sets is not substantial⁷. Recently, C-IFS have found applications in the field of business technology, particularly in the study of how to optimize the process of adopting robotics in sustainable logistics, where they exhibit a more substantial capacity for uncertainty modeling compared to the classical fuzzy model⁸. C-IFS have also been applied in combination with entropy-based flavorings of SWARA and COCOSO in the assessment of renewable energy systems, thereby adding to the stability of decisions in the presence of multiple, incompatible criteria⁹. Based on the evidence provided in past work, it is evident that hybrid MCDM architectures with a high-tech fuzzy environment can be effectively used in prioritizing renewable energy initiatives, facilitating sustainable development¹⁰. Later research also combines cubic picture fuzzy modeling with blockchain and the metaverse to address the uncertainty of supply-chain systems¹¹.

MCDM approaches provide systematic models over which the criteria used in management are analyzed and combined¹². The COCOSO approach is usually used to integrate disparate factors and make perfect decisions, though it provides poor performance where higher discrimination is required because of the interrelationship among them¹³. The COCOFISO method was designed as a reaction to this limitation, and it presents refined normalization methods and less strict weighting frameworks, leading to more accurate decisions. The current research uses the C-IF COCOFISO model to maximize the short-video tactics in the university initiatives to promote intelligent communication. The given experience in practice shows how, using this strategy, it is possible to assess various vital points and develop short-video strategies helping to popularize the campus culture in a new quality of precision, sustainability, and alignment due to the institution.

Short video platforms and intelligent communication for campus culture construction

Recent research indicates that a variety of subjects regarding short video platforms and intelligent communication in higher education may be studied: the engagement of students, efficacy of the learning process, or cultural progress. As demonstrated by Xu and Li¹⁴, the short videos made by TikTok lead to significant effects on the learning motivation of university students and the culture of the university campuses. Li, Geng, and Wu¹⁵ assert that the use of short-form videos can improve academic participation and, at the same time, raise academic stress, indicating that strategic integration should be equal. In their study, Zhou et al.¹⁶ explain the way intelligent recommendation systems and interactive options in platforms like TikTok enhance learning interaction and effectiveness in communication across students. Zhu et al.¹⁷ confirm that the implementation of brief videos in online flipped classrooms contributes to improved student learning, but also cultivates the culture of learning on the campus. Citing a similar study conducted by Ye et al.¹⁸, it can be pointed out that excessive use of short videos is indicated to be linked with the perceived ineffectiveness of learning in vocational students, which underlines the need to design strategies optimally. In its totality, these studies confirm the necessity of short video platforms in developing smart communication and building the internal campus culture, as short video platforms allow learners to interact, culture to spread, and students to engage more deeply when repurposed deliberately and strategically.

The COCOFISO method within MCDM applications

In decision analysis, MCDM belongs to the category of strategies because no single universality of such methods exists that can organize the simultaneous presence of diverse criteria in complex strategic analyses, such as the optimization of short-video strategies in intelligent communication for the development of campus culture. These eminent methods are TOPSIS, VIKOR, PROMETHEE and MOORA. Despite the valuable features of the tools in terms of effective decision-making under uncertainty, their applicability is limited to optimization of decision-making in the face of hesitation or judgment conflicts, in the realm of digital media strategy optimization. Whenever that happens, the COCOFISO approach is often desirable as it tends to synthesize various compromising solutions, thus creating a balanced decision that is more reliable and stable. Special attention ought to be drawn to the C-IF COCOFISO method, where, expediently, the expert disagreements and the reluctance to evaluation are segregated and adequately dealt with, ensuring accuracy in decisions. Its effectiveness has been proven in many studies with regard to a variety of areas. Gabriel Rasoanaivo et al.¹³ was shown to have been used as an expert system with the ability to circumvent the shortfalls of traditional COCOSO in complicated expert-based rating. Sen and Toksoy¹⁹ enhanced grey COCOFISO, introducing grey numbers to make supplier selection and decisions more accurate. Rasoanaivo and Tata²⁰ utilized COCOFISO to rank Madagascar universities, whereas Nirinarivelo and Rasoanaivo²¹ analyzed issues of employment conditions in various regions using COCOFISO.

Due to its application in the realm of industrial and safety assessment, Wang et al.²² used COCOSO to advise underground mining sensor selection, with the paper highlighting its applicability in technical decision-making contexts. Haseli et al.²³ have used COCOSO to design structures of sustainable urban transportation in the context of a group decision. Supplier ranking models were developed by Bihari et al.²⁴ with the help of generalized trapezoidal fuzzy-COCOSO techniques and have since been used to assess liquefied natural gas storage tank inspection²⁵ and even in the improvement of knowledge management in fashion supply chains²⁶. Zheng et al.²⁷ presented a decision-making model which combined interval-valued q-rung orthopair fuzzy sets and COCOSO, and Maliha et al.²⁸ estimated antibacterial edible sodium alginate packaging by integrating entropy with the COCOSO strategy. Although TOPSIS and VIKOR are operable in decision analysis, they often face difficulties in solving incompleteness and inconsistent expert evaluation in complicated strategic environments. COCOFISO, in its turn, accumulates several solutions which are based on compromising to draw a balanced pair of decisions. This study also promotes the use of C-IF COCOFISO as a better decision-support methodology, since it allows taking into consideration hesitancy, dealing with vague evaluations, and resolving experts’ disagreements effectively. It is, therefore, very applicable in maximizing the short-video plans to enhance smart communication and culture in the campus setting in higher schools.

Identified gaps and study rationale

The current paper supports the development of an advanced decision-support model that would activate short-video communicative work at universities and create a culture on campus. Traditional approaches to this area of operation often lack flexibility, responsiveness to new trends, and alternative methods for providing individualized messages, thereby compromising their effectiveness with digitally engaged student audiences. The mechanisms are mostly linear and standard, scripted processes that do not incorporate multidimensional considerations, such as cultural fit, ethical sensitivity, and technological flexibility. In contrast, short-video environments thrive in a highly dynamic digital ecosystem, where student behavior is hugely dynamic, constantly driven by the rapidity of preference shifts, fluctuations in participation rates, and rapid changes in content popularity. Such dynamic complexity poses a challenge for deterministic or hard-coded evaluation processes. Based on this, the research paper employs a complex evaluation approach, referred to as COCOFISO, which combines discussions of several evaluation aspects. Through such a methodology, communication teams in universities can develop a plan for short videos that align with the flexibility of the technology, budget, assessment, cultural relevance, ethics, and student-friendly appeal. Considering the fundamental uncertainty, reservations, and mixed opinions that are common in dynamic involvement, the framework applies C-IFS to provide a more adaptive and nuanced analogy compared to mainstream Multiple-Criteria Decision-Making approaches. A practical application of the C-IF COCOFISO model is demonstrated in the experimental trial, where the comparison of alternative short-video strategies built according to substantive evaluation criteria is the primary focus. The efficiency and the aptness of the model are then justified by sensitivity and comparison tests. This study makes several significant contributions. To begin with, this research study proposes a new framework, known as the C-IF COCOFISO model, which can serve as a guided, scientific review in creating a short video strategy for an organization on campus, thereby contributing to a more vibrant campus culture. Based on the C-IFSs, the proposed framework is depicted as helpful in managing uncertainties; therefore, the process of vague, hesitant, and conflicting evaluations is demonstrated to be performed more accurately compared to traditional MCDM methods. The consequence is a more precise decision-making process and steadier strategic outcomes in the institutional arenas. Second, unlike previous literature on the topic, whose works are limited to short video content production processes or the level of engagement of various users, the investigation operationalizes state-of-the-art decision-making processes to advance long-lasting and efficient campus culture promotion. These results provide advisable steps for university administrators, digital media professionals, and communication strategists to consider, who can then utilize short video strategies to promote new forms of communication, cultural integration, and the university’s institutional status.

Research goals and key contributions

Based on this, the research paper employs a complex evaluation approach, referred to as COCOFISO, which combines discussions of several evaluation aspects. Through such a methodology, communication teams in universities can develop a plan for short videos that align with the flexibility of the technology, budget, assessment, cultural relevance, ethics, and student-friendly appeal. Considering the fundamental uncertainty, reservations, and mixed opinions that are common in dynamic involvement, the framework applies C-IFS to provide a more adaptive and nuanced representation compared to mainstream Multiple-Criteria Decision-Making approaches. Unlike IFWA, which aggregates membership and non-membership values in a simple weighted manner, or CPFS and CIF-HWA, which offer limited ability to model hesitation and complex interdependencies in preferences, C-IFS simultaneously captures membership, non-membership, and hesitation degrees on a circular domain. This richer representation enables COCOFISO to preserve the geometric relationships between evaluations, provide finer discrimination between closely ranked strategies, and generate rankings that remain robust and context-sensitive in rapidly changing engagement environments. A practical application of the C-IF COCOFISO model is demonstrated in the experimental trial, where the comparison of alternative short-video strategies built according to substantive evaluation criteria is the primary focus. The efficiency and the aptness of the model are then justified by sensitivity and comparison tests.

Organization of the research work

The present research study is structured as follows: in Sect. 2, the methodology of the research is presented, including the concepts of C-IFS and the detailed description of C-IF COCOFISO approach, along with the description of the steps that were taken to implement the described method in the given research to measure and optimize the short-video strategies related to improving the intelligent communication in the field of university campus culture development. Section 3 reveals the empirical evidence obtained after applying the suggested model in a real-life situation. Section 4 will perform a comparative analysis concerning a set of other identified MCDM methods, a discussion of the theoretical and practical importance of the results, and sensitivity analysis by changing the parameter values to test the robustness and trustworthiness of the solution. The key strengths and limitations of the proposed methodology are also located in this section. Section 5 categorically summarizes the findings of the research and points to the directions in future research on the optimization of short-video strategies and intelligent campus-communication initiatives.

Preliminaries

The current exposition outlines the theoretical premises that will be essential in the development of a structure that will maximize the usage of short-video approaches in universities. In particular, it starts the discussion by defining campus-integrated strategic systems and identifying their operational features, in terms of being able to deal with uncertainty and strategic hesitation in particular. The analysis subsequently leaves the basic double chain of the COCOFISO method for a more advanced version of the COCOSO system method that embraces the hierarchical notation style and complex aggregation functions at decision-making. Taken together, these ideas provide the necessary theoretical structure to utilize the C-IF COCOFISO model to improve short-video strategies, thus being part of the development of intelligent communicative practices and facilitating the establishment of a campus culture.

Definition 1

²⁹A C-IFS is denoted as $\:{C}_{\mathfrak{d}}=\left(\left({\mathfrak{p}}_{\mathfrak{d}},{\mathfrak{q}}_{\mathfrak{d}}\right);{\mathfrak{r}}_{\mathfrak{d}}\right)$ is characterized by a three-component structure, where $\:{\mathfrak{p}}_{\mathfrak{d}}$ indicates the MD, $\:{\mathfrak{q}}_{\mathfrak{d}}$ indicates the NMD and $\:{\mathfrak{r}}_{\mathfrak{d}}$ Indicates the corresponding circular radius. These components satisfy the condition: $\:0\le\:{\mathfrak{p}}_{\mathfrak{d}}+{\mathfrak{q}}_{\mathfrak{d}}\le\:1$ and $\:{\mathfrak{r}}_{\mathfrak{d}}\in\:\left[\text{0,1}\right]$. For the hesitancy degree $\:{\text {H}}_{C}$ is determined by $\:{\text {H}}_{C}=1-{\mathfrak{p}}_{\mathfrak{d}}-{\mathfrak{q}}_{\mathfrak{d}}.$ The triplet $\:\left(\left({\mathfrak{p}}_{\mathfrak{d}},{\mathfrak{q}}_{\mathfrak{d}}\right);{\mathfrak{r}}_{\mathfrak{d}}\right)$ is known as a circular intuitionistic fuzzy value (IFV).

Definition 2

²⁹Consider $\:{C}_{{\mathfrak{d}}_{1}}=\left(\left({\mathfrak{p}}_{1},{\mathfrak{q}}_{1}\right);{\mathfrak{r}}_{1}\right)$ and $\:{C}_{{\mathfrak{d}}_{2}}=\left(\left({\mathfrak{p}}_{2},{\mathfrak{q}}_{2}\right);{\mathfrak{r}}_{2}\right)$ be the C-IFS. Then, the fundamental operational laws defined by the C-IFV are;

$$\:{C}_{{\mathfrak{d}}_{1}}\oplus\:{C}_{{\mathfrak{d}}_{2}}=\left(\left({\mathfrak{p}}_{1}+{\mathfrak{p}}_{2}-{\mathfrak{p}}_{1}.{\mathfrak{p}}_{2},{\mathfrak{q}}_{1}.{\mathfrak{q}}_{2}\right);{\mathfrak{r}}_{1}+{\mathfrak{r}}_{2}-{\mathfrak{r}}_{1}.{\mathfrak{r}}_{2}\right)$$

$$\:{C}_{{\mathfrak{d}}_{1}}\otimes\:{C}_{{\mathfrak{d}}_{2}}=\left(\left({\mathfrak{p}}_{1}.{\mathfrak{p}}_{2},{\mathfrak{q}}_{1}+{\mathfrak{q}}_{2}-{\mathfrak{q}}_{1}.{\mathfrak{q}}_{2}\right);{\mathfrak{r}}_{1}.{\mathfrak{r}}_{2}\right)$$

$$\:\partial\:{C}_{{\mathfrak{d}}_{1}}=\left(\left(1-{\left(1-{\mathfrak{p}}_{1}\right)}^{\partial\:},{\left({\mathfrak{q}}_{1}\right)}^{\partial\:}\right);1-{\left(1-{\mathfrak{r}}_{1}\right)}^{\partial\:}\right)$$

$$\:{\left({C}_{{\mathfrak{d}}_{1}}\right)}^{\partial\:}=\left(\left({\mathfrak{p}}_{1}^{\partial\:},1-{\left(1-{\mathfrak{q}}_{1}\right)}^{\partial\:}\right);{\mathfrak{r}}_{1}^{\partial\:}\right)$$

Definition 3

²⁹Consider $\:{C}_{{\mathfrak{d}}_{1}}=\left(\left({\mathfrak{p}}_{1},{\mathfrak{q}}_{1}\right);{\mathfrak{r}}_{1}\right)$ be the C-IFV. The score and accuracy function are defined as given in Eqs. (1) and (2).

$$\:\begin{array}{c}{\mathcal{S}}_{{C}_{\mathfrak{d}}}=\left({\mathfrak{p}}_{1}-{\mathfrak{q}}_{1}\right)\times\:{\mathfrak{r}}_{1}\end{array}\:$$

(1)

$$\:\begin{array}{c}{\mathcal{A}}_{{C}_{\mathfrak{d}}}=\left({\mathfrak{p}}_{1}+{\mathfrak{q}}_{1}\right)\times\:{\mathfrak{r}}_{1\:}\end{array}$$

(2)

CIF-COCOFISO framework

The given study proposes a new, practical approach to incorporating COCOFISO methodologies into a Circular Intuitionistic Fuzzy model in order to maximize brief-video strategies to facilitate the construction of an intelligent campus culture. Figure 1 provides an exhaustive flow chart of the suggested methodology.

Fig. 1

Flowchart Illustrating the Proposed Methodology.

Implementation of C-IFV-Based COCOFISO methods

The algorithm presented below is based on the approach presented above:

Step 1

The first step is to define the set of alternatives, criteria and weights of the requirements in suitable linguistic terms to develop the initial decision matrix.

$$\:{\left({\mathfrak{U}}_{\Omega}\right)}_{m\times\:n}=\begin{array}{c}{R}_{1}\\\:{R}_{2}\\\:\vdots\\\:{R}_{i}\end{array}\left[\begin{array}{c}\begin{array}{c}{\mathcal{C}}_{1}\\\:\left({\mathfrak{p}}_{11},{\mathfrak{q}}_{11};{\mathfrak{r}}_{11}\right)\\\:\left({\mathfrak{p}}_{21},{\mathfrak{q}}_{21};{\mathfrak{r}}_{21}\right)\end{array}\\\:\vdots\\\:\left({\mathfrak{p}}_{m1},{\mathfrak{q}}_{m1};{\mathfrak{r}}_{m1}\right)\end{array}\:\:\:\:\begin{array}{c}\begin{array}{c}{\mathcal{C}}_{2}\\\:\left({\mathfrak{p}}_{m2},{\mathfrak{q}}_{m2};{\mathfrak{r}}_{m2}\right)\\\:\left({\mathfrak{p}}_{m2},{\mathfrak{q}}_{m2};{\mathfrak{r}}_{m2}\right)\end{array}\\\:\vdots\\\:\left({\mathfrak{p}}_{m2},{\mathfrak{q}}_{m2};{\mathfrak{r}}_{m3}\right)\end{array}\:\:\:\:\begin{array}{c}\begin{array}{c}\dots\:\\\:\dots\:\\\:\dots\:\end{array}\\\:\ddots\:\\\:\cdots\:\end{array}\:\:\:\:\:\:\begin{array}{c}\begin{array}{c}{\mathcal{C}}_{j}\\\:\left({\mathfrak{p}}_{1n},{\mathfrak{q}}_{1n};{\mathfrak{r}}_{1n}\right)\\\:\left({\mathfrak{p}}_{2n},{\mathfrak{q}}_{2n};{\mathfrak{r}}_{2n}\right)\end{array}\\\:\vdots\\\:\left({\mathfrak{p}}_{mn},{\mathfrak{q}}_{mn};{\mathfrak{r}}_{mn}\right)\end{array}\:\right]$$

Step 2

To measure the possible solution, the alternative matrix is normalized using the formula in Eq. (3), depending benefit type or cost type criteria^30,31.

$$\:\begin{array}{c}{\mathcal{F}}_{ij}=\left(\left(\frac{{\mathfrak{p}}_{ij}}{\sqrt{\sum\:_{i=1}^{m}{\left({\mathfrak{p}}_{ij}\right)}^{2}}},\frac{{\mathfrak{q}}_{ij}}{\sqrt{\sum\:_{i=1}^{m}{\left({\mathfrak{q}}_{ij}\right)}^{2}}}\right);\frac{{\mathfrak{r}}_{ij}}{\sqrt{\sum\:_{i=1}^{m}{\left({\mathfrak{r}}_{ij}\right)}^{2}}}\right)\end{array}$$

(3)

Step 3

This method offers two methods of combining the values of the weight of the criteria in the process of decision-making. The initial method computes the sum of the power-weighted normalized matrix values $\:\left({\dot{P}}_{i}\right)$, whereas the subsequent method determines the matrix product with the weight values $\:\left({\text {S}}_{i}\right)$ as given in Eq. (4).

$$\:\begin{array}{c}{\text {S}}_{i}=\left(\left(\sum\:_{j=1}^{n}{w}_{j}{\mathfrak{p}}_{ij},\sum\:_{j=1}^{n}{w}_{j}{\mathfrak{q}}_{ij},\right);\sum\:_{j=1}^{n}{w}_{j}{\mathfrak{r}}_{ij}\right)\end{array}$$

(4)

The $\:{\text {S}}_{i}$ value is derived from the grey relational generation strategy, while the $\:{\dot{P}}_{i}$ value is computed utilizing the multiplication method of the Weighted Aggregated Sum Product Assessment () technique.

Step 4

The $\:\text {S}$ and $\:\dot{P}$ values are determined by considering the relative significance of all alternatives using the three evaluation scoring approaches defined in Eqs. (5)–(7).

$$\:\begin{array}{c}{\mathcal{K}}_{ia}=\frac{{\dot{P}}_{i}+{\text {S}}_{i}}{\sum\:_{i=1}^{m}\left({\dot{P}}_{i}+{\text {S}}_{i}\right)}\end{array}$$

(5)

$$\:\begin{array}{c}{\mathcal{K}}_{ib}=\frac{{\dot{P}}_{i}+{\text {S}}_{i}}{\frac{{\dot{P}}_{i}}{1+{\dot{P}}_{i}}+\frac{{\text {S}}_{i}}{1+{\text {S}}_{i}}}\end{array}$$

(6)

$$\:\begin{array}{c}{\mathcal{K}}_{ib}=\frac{\delta\:\left({\text {S}}_{i}\right)+\left(1-\delta\:\right)\left({\dot{P}}_{i}\right)}{\left(\delta\:\underset{i}{\text{max}}{\text {S}}_{i}+\left(1-\gamma\:\right)\underset{i}{\text{max}}{\dot{P}}_{i}\right)};0\le\:\delta\:\le\:1\end{array}$$

(7)

Here,

$\:{\mathcal{K}}_{ia}$ refers to the Weighted Sum Method (S)³² and Weighted Product Method (P)³³ scores. $\:{\mathcal{K}}_{ib}$ is the summation of both relative scores of S and P. and $\:{\mathcal{K}}_{ic}$ points to the compromised balancing measure between those of the S and the P modes.

Step 5

The scoring function in Eq. (9), as presented in Eq. (1), is then applied to the calculation of the $\:{\mathcal{K}}_{i}$ values which will define which alternatives are to be ranked in preference.

$$\:\begin{array}{c}{\mathcal{K}}_{i}=\left({\left({\mathcal{K}}_{ia}*{\mathcal{K}}_{ib}*{\mathcal{K}}_{ic}\right)}^{\frac{1}{3}}+\frac{1}{3}\left({\mathcal{K}}_{ia}+{\mathcal{K}}_{ib}+{\mathcal{K}}_{ic}\right)\right)\end{array}$$

(8)

The complete pseudo-code of the proposed algorithm is provided as Supplementary File for reference.

Optimization of short video strategies for campus culture construction

The short videos are electronically created and distributed media that are usually of short length, innovative visual content and interactive. They are a fundamental asset of contemporary communication and accomplish vital functions in learning and culture, as well as in the involvement of communities within university settings. In the campus culture building case, the short videos record and share the non-material cultural properties like traditions, student activities, institutional values and creative performances. The high pace of the appearance of modern digital communication technologies requires the planning of a short video implementation process based on the strategy of optimal optimization, since in this way, the application of short videos would be practical in terms of solving the problem of intelligent communication and developing cultural integration in students. Figure 2 shows the representation of the decision-making process to optimize short videos in universities in detail.

Fig. 2

The Decision-Making Process to Optimize Short Videos in Universities.

It accentuates the core technological aspects that involve artificial intelligence to recommend content, cloud computing to store and access data, cybersecurity to provide protection to data, and digital twins technologies to mimic interactive video scenarios. An integration of these technologies becomes a methodological operation of short video strategies corresponding with the principles of the decision algorithm in the course of effective building of campus culture. Nevertheless, there are a few problems associated with maximizing short video strategies, including doubts in resource allocation, dynamism of changes in the platform algorithms, data privacy, and cultural relevance. Technical and organizational uncertainties are observed since the digital media standards are dynamic, the platform policies have to be altered regularly, and the cultural authenticity has to be considered during the sharing of content. Hence, to choose the correct short video applications, a strong decision-making model is needed to meet the uncertainties and fit different institutional circumstances. Short video optimization is complex, and MCDM methods must be employed. Through the application of the C-IF COCOFISO method, the decision-makers will be able to run uncertain data inputs in a systematic manner and compare the strategic alternatives on the basis of pre-determined criteria. Compared with conventional fuzzy set models, C-IFSs allow the incorporation of a degree of membership, a degree of abstinence, and a non-membership degree, hence providing an enhanced picture of evaluating and ranking alternative short video strategies based on their level of accuracy and reliability.

Numerical example

In this section, the employment of the CIF-COCOFISO approach is revealed concerning short video as one of the strategies that can be optimized to contribute to the intelligent communication process in the campus culture realm of a university. One of the major universities plans to review the different short video techniques in order to determine the best-suited methods that could be applied in propagating the cultural values, expanding the student creativity and enhancing the institutional identity. Technological compatibility, financial viability, cultural compatibility, ethics, long-term viability, and the ability to engage the students are other things that are considered when making strategic selections. It should be reasonable to select the particular strategy and adhere to long-term educational communication objectives to make the choice long-term and productive.

Twenty-seven alternatives in this application occur using six decision criteria. Evaluation of the criteria is described below:

• $\:{{\acute{\rm C}}}_{1}:\:$Technological adaptability: Assesses the ability of short video strategies to integrate with emerging digital technologies, adapt to platform algorithm changes, and remain resilient against future technological shifts.

• $\:{{\acute{\rm C}}}_{2}:\:$Content quality and creativity: Evaluates the clarity, production quality, and creativity embedded within short video content to maintain high engagement standards.

• $\:{{\acute{\rm C}}}_{3}:\:$Cultural relevance and authenticity: Measures how effectively short video content represents university values and cultural traditions without misinterpretation or cultural dilution.

• $\:{{\acute{\rm C}}}_{4}:\:$Student engagement and accessibility: Determine the potential of short videos to engage diverse student groups by ensuring accessibility across devices and learning contexts.

• $\:{{\acute{\rm C}}}_{5}:\:$Cost-effectiveness Examines the financial feasibility of developing and maintaining short video strategies relative to their expected impact and institutional budget constraints.

• $\:{{\acute{\rm C}}}_{6}:\:$Data security and ethical considerations: Evaluate how well the strategy ensures student data protection, copyright compliance, and ethical standards in digital communication.

The twenty-seven short video strategy alternatives, as inquired by the research, are:

• $\:{\varvec{A}}_{1}$: AI-based Content Personalization.

• $\:{\varvec{A}}_{2}$: Student-Generated Video Campaigns.

• $\:{\varvec{A}}_{3}$ Short Video Challenges for Cultural Themes.

• $\:{\varvec{A}}_{4}$ Virtual Campus Tours via Short Videos.

• $\:{\varvec{A}}_{5}$: Short Video Announcements and Updates.

• $\:{\varvec{A}}_{6}$: Gamified Short Video Learning Modules.

• $\:{\varvec{A}}_{7}$: Faculty-Student Collaborative Video Projects.

• $\:{\varvec{A}}_{8}$: Augmented Reality Integrated Short Videos.

• $\:{\varvec{A}}_{9}$: Motivational Short Video Series.

• $\:{\varvec{A}}_{10}$: Peer-to-Peer Educational Short Videos.

• $\:{\varvec{A}}_{11}$: Alumni Stories in Short Video Format.

• $\:{\varvec{A}}_{12}$: Live Short Video Streaming Events.

• $\:{\varvec{A}}_{13}$: Interactive Poll-Based Short Videos.

• $\:{\varvec{A}}_{14}$: Short Videos for Mental Health Awareness.

• $\:{\varvec{A}}_{15}$: Sustainability Awareness Short Videos.

• $\:{\varvec{A}}_{16}$: Short Video Tutorials for Campus Facilities.

• $\:{\varvec{A}}_{17}$: Cultural Festival Highlights in Short Videos.

• $\:{\varvec{A}}_{18}$: Short Videos Featuring Faculty Insights.

• $\:{\varvec{A}}_{19}$: Digital Storytelling through Short Videos.

• $\:{\varvec{A}}_{20}$: Short Video Contests for Creativity Promotion.

• $\:{\varvec{A}}_{21}:$ Language Learning Short Videos.

• $\:{\varvec{A}}_{22}:$ Safety Awareness Campaigns in Short Video Form.

• $\:{\varvec{A}}_{23}:$ Career Development Tips via Short Videos.

• $\:{\varvec{A}}_{24}:$ Community Service Project Highlights.

• $\:{\varvec{A}}_{25}:$ Wellness and Fitness Tips in Short Video Format.

• $\:{\varvec{A}}_{26}:$ Research Project Summaries via Short Videos.

• $\:{\varvec{A}}_{27}:$ Entrepreneurship and Innovation Stories in Short Videos.

To maximize these strategies, a systemized decision framework must be put in place that will allow the management of uncertainties, cultural authenticity, and financial limits. The six criteria chosen are the key factors to be focused on in evaluating the short video strategies, providing the decision-making procedure with an emphasis on technological feasibility, including students, preserving their cultural autonomy, and not being cost-ineffective. In the given application, a panel of decision-makers who are the university media coordinators, the members of the artistic committee, as well as a team of communication strategists, was used to analyze the weights of the criteria according to their areas of expertise. The weights of the criteria were hypothetically determined as $\:{w}_{i}=(0.11,\:0.13,\:0.15,\:0.1,\:0.28,\:\text{0.15,0.08})$, reflecting the relative importance of each criterion. The normalization condition $\:\sum\:_{i=1}^{n}{w}_{i}=1$ ensures balanced significance distribution among criteria in the evaluation process. The case study demonstrates how the CIF-COCOFISO method effectively evaluates and ranks short video strategy alternatives to support intelligent communication and foster a vibrant, culturally cohesive campus environment. The integration of C-IFSs enhances decision precision and reliability by managing ambiguous and hesitant expert judgments during strategy prioritization. The steps of the COCOFISO model for optimizing short video strategies in campus culture construction are outlined below.

Step 2

Construct the decision matrix by defining the set of short video strategy alternatives using the linguistic terms provided in Table 1.

Table 1 Evaluation of alternatives by linguistic terms.

Step 2

Table 1 presents the linguistic terms selected by decision-makers for evaluating the short video strategies using C-IFVs. The alternatives are assessed against each criterion through the constructed decision matrix based on the defined linguistic preferences shown in Table 2. This assessment approach ensures both reliable and consistent decision-making throughout the evaluation process.

Table 2 Circular intuitionistic fuzzy decision matrix.

Step 3

The decision matrix is normalized according to Eq. (3), and the results are presented in Table 3.

Table 3 Normalization for the decision matrix.

Step 4

The aggregation of the criteria weight values is calculated by determining both the sum of the product of the normalized matrix and the weight values $\:{(\text {S}}_{i})$, and the sum of the power-weighted normalized matrix values $\:\left({\dot{P}}_{i}\right)$, using Eq. (4). The results are presented in Table 4.

Table 4 Procedures for aggregating weights across Criteria.

Step 5

In this step, the $\:\text {S}$ and $\:\dot{P}$ values are aggregated by assessing the relative weight of each alternative using the three evaluation scoring approaches defined by Eqs. (5)–(7), as presented in Table 5.

Table 5 Appraisal score approaches.

Step 6

The alternatives are ranked according to their calculated ki values using Eq. (8), with the rankings shown in Table 6.

Table 6 Ranking of the alternatives.

Results

The application of the C-IF COCOFISO method facilitated effective decision-making for optimizing short video strategies aimed at enhancing campus culture and intelligent communication. The evaluation process generated ranked alternatives based on their overall performance across the defined decision criteria. According to the results, $\:{A}_{9}$ achieved the highest score, indicating it as the most suitable strategy for promoting intelligent communication and cultural cohesion within the university. This was followed by $\:{A}_{15}$ and $\:{A}_{14}$, which demonstrated strong alignment with the evaluation criteria and were ranked second and third, respectively. Other strategies like $\:{A}_{4}$ and $\:{A}_{19}$ were as well placed among the top five which demonstrated their ability to make tremendous contribution to the construction of the campus culture whenever they are combined and utilized well. The ranking of the alternatives is depicted in the form of a graph, as depicted in Fig. 3. On the other hand, $\:{A}_{5}$, $\:{A}_{25}$, and $\:{A}_{26}$ ended last position because they delivered less superior in such clusters as cultural relevance, affordability, and technological flexibility. These substitutes can be restricted by the aspect of feasibility of implementation, financial viability, or attraction to students. Alternatives in the middle such as $\:{A}_{1}$, $\:{A}_{17}$, and $\:{A}_{22}$, showed moderate result. They fulfilled some of the evaluation criteria, but were not fully aligned on all the factors of the decision, as the result, they were moderate but not optimal decisions. CIFCOCOFISO model came in handy in resolving the uncertainties and complex judgments that are present in strategic evaluation of short video options. This process would guarantee accurate ranking, and decision-makers would determine the best shorts videos strategies, which can match the institutional goals, technology, and cultural authenticity in the undertaking of campus culture building initiatives in universities.

Fig. 3

Representation of ranking results Using the C-IF COCOFISO method.

Computational efficiency

Critical evaluation of the power of computation of the C-IF COCOFISO shows that it can handle sometimes challenging decision-making involving optimizing the evaluation of short video plans in the settings of modern construction of the campus culture in the university. The proposed circular version of intuitionistic fuzzy sets, combined with the COCOFISO methodology, allows extensive consideration of alternative assessment and criteria ranking, even in the situation of extreme uncertainty and high communication dynamics. It is worth highlighting that implementation of both weighted aggregation and fusion mechanism into the model results in improved computational stability and flexibility, thus offering accurate prioritization of strategic options. The approach illustrates that it can produce trustworthy solutions without incurring impractical amounts of computation costs, which makes the method suitable towards large-scale strategy optimization processes. Its strong data processing and fast convergence characteristics also allow the convenient handling of large data sets, including very large sets of alternatives and criteria, without employing unacceptable computational pressure. Therefore, the C-IF COCOFISO approach becomes one of the effective mechanisms of decision-making assistance in universities desiring to adopt sustainable and innovative short video plans to improve the campus landscape enrichment and smart communication facilitation.

Discussion

Statistical analysis

To ascertain the robustness and sensitivity of the proposed aggregation paradigm, the initial ranking results obtained at $\:\delta\:\:=\:0.1$ were compared with those derived by incrementally increasing the $\:\delta\:$-value up to $\:0.9$. Analysis of the reordered score values reveals that the top-performing alternatives $\left( {A_{9} ,~A_{{24}} ,~A_{{14}} ,~A_{{19}} ,~A_{4} } \right)$ exhibit complete rank preservation across all investigated parametric conditions, while the alternatives $\left( {A_{1} ,~A_{{18}}} \right)$ maintain a unchanged ordering, highlighting a strongly stable ranking core. In comparison, alternatives $\left( {A_{19} ,~A_{{27}}} \right)$ demonstrate slight positional modifications in response to varying δ, thereby underlining a greater parameter sensitivity in the lower-ranked portion of the preference structure.

To statistically uphold these observations, the Spearman rank correlation coefficient³⁴ was adopted as a comparative measure between the baseline ranking vector and those obtained under the perturbed parameter values. This coefficient is given by Eq. (9).

$$\:\begin{array}{c}{r}_{R}=1-\frac{6\sum\:_{i}{d}_{i}^{2}}{n\left({n}^{2}-1\right)}\:\:\end{array}$$

(9)

where $\:n$ denotes the total number of alternatives and $\:{d}_{i}\:$represents the difference in the ranking position of the $\:{i}^{th}$ alternative between two corresponding $\:\delta\:$-values. The statistical validation results indicate that $\:{d}_{i}=0$ for the majority of the alternatives, and the magnitude of $\:{d}_{i}$​ for the remaining ones, the amount is very small. As a result, the computed Spearman correlation coefficients remain extremely close to $\:+1$ for all parameter comparisons, signifying a very strong positive association between the ranking vectors obtained under different $\:\delta\:$ settings. These outcomes confirm that the proposed model yields highly reliable, stable, and robust decision results, even when subjected to parameter variations, thereby establishing the practical soundness of the developed methodology. Across all three parameter intervals, the calculated weighted Spearman’s rank coefficients consistently exhibit values that lie within the “very strong positive” association range, thereby confirming the high robustness of the proposed model. In the lower interval $\:(\delta\:\:=\:0.1-0.3)$, only negligible rank changes were observed, indicating an almost perfect alignment with the original ranking vector. As δ progresses into the medium range $\:(\delta\:\:=\:0.3-0.6)$, the correlation results still reflect a strong concordance, with only minor shifts occurring in the middle-to-lower ranked alternatives. Even under the highest parameter settings $\:(\delta\:\:=\:0.6-0.9)$, the coefficients continue to indicate a solidly strong relationship, highlighting that the proposed ranking mechanism is capable of maintaining consistency and reliability even in the presence of greater perturbations.

Comparison analysis

This part critically evaluates the functionality of the C-IF COCOFISO method to optimize short video strategies in the intelligent campus culture communication with reference to the following MCDM techniques: MARCOS, WASPAS, CODAS, MAIRCA, and COPRAS. The comparative results were summarized in Table 7 according to the underlying theoretical framework of the method and according to the practical usefulness of the method to prepare a university communication strategy. The C-IF COCOFISO has a high degree of ability to process the information of uncertainty and hesitation. Unlike WASPAS³⁵ and MAIRCA³⁶, which do not have integrated fuzzy hesitation processing, COCOFISO integrates circular intuitionistic fuzzy data with a time-varying aggregation to help in making realistic decisions in the creative and educational media planning situation. The MARCOS approach³⁷, despite being effective in proportional utility analysis, fails to make adaptive weighting changes, which are necessary in the presence of a set of multiple objectives of campus communications that compete with each other. In the same way, CODAS³⁸ admits of failure to support large-scale linguistic data incursions of multiple stakeholders across the campus, something that may be essential in distance-based assessments to realize methodologically sound evaluative insights. COPRAS³⁹ has a high priority ranking, but it struggles with ambiguous or neutral opinions, as in the case of short videos strategy analysis. In the present research report, the C-IF COCOFISO framework is compared with a few higher-order fuzzy techniques: the T-Spherical Fuzzy Interactive Dubois-Prade Information Aggregation Approach⁴⁰ and the Cubic Picture Fuzzy Fairly Aggregation Operators method⁴¹. Despite the overall strength of uncertainty modelling that both of these methods provide in environmental and ecological contexts, the application of C-IF COCOFISO related to dynamic cultural communication scenarios is expanded. Thus, the research proves the flexibility of the method in decision-making contexts of various natures. Meanwhile, C-IF COCOFISO incorporates the neutral degrees of membership to allow balancing in the evaluation framework and demonstrates not only decisive preferences, but also uncertain expert opinion. Generally, C-IF COCOFISO exhibited high rank stability, operational accuracy, and computational feasibility in areas of wide strategic options, which makes it the most suitable tool in universities in their quest of a systematic streamlining of their short video programs offerings.

Table 7 Comparative evaluation of decision-making techniques.

Table 8 provides comparative assessment of methods in MCDM available at the moment and their related barriers toward realization of efficient strategy to the optimization of short video in intelligent campus communication. Methods like MARCOS, WASPAS, CODAS, MAIRCA and COPRAS are common and normally lack options to include sophisticated uncertainty modelling, levels of hesitation, or adaptive weighting components that are critical to subtle decisions in dynamic cultural environments of university situations. On the other hand, the C-IF COCOFISO approach fulfils all the gaps by combining the concept of C-IF with contextual sensitive aggregation and hence it yields a strong, flexible and accurate framework to support complex calculations of digital media strategy analysis in campus settings.

Table 8 Comparison analysis with the existing MCDM methods.

Sensitive analysis

The C-IF COCOFISO methodology proves its robustness as a decision-support framework in optimizing short video strategies for intelligent communication within university campus culture construction. This is validated through the sensitivity analysis, which tests the δ parameter values from $\:0.1$ to $\:0.9$, as illustrated in Fig. 4. The results indicate that alternative $\:{A}_{9}$ consistently achieved the highest-ranking scores across all $\:\delta\:$ values, highlighting its strong suitability as the leading short video strategy under diverse evaluation conditions. Similarly, alternatives $\:{A}_{4}$, $\:{A}_{14},\:{A}_{19},$ and $\:{A}_{24}$ maintained high positive scores with minimal fluctuations, proving their stability and strategic feasibility within campus culture communication enhancement. Conversely, alternatives $\:{A}_{5},\:{A}_{10},$ and $\:{A}_{15}$ exhibited consistently negative scores across all δ variations, indicating their limited potential as viable strategies for intelligent communication purposes. Moderate ranking shifts were observed for alternatives $\:{A}_{3},\:{A}_{8},\:{A}_{13},\:{A}_{18},\:{A}_{20},\:{A}_{23}$, and $\:{A}_{25}$, suggesting that their selection effectiveness is highly sensitive to parameter adjustments, and their implementation should be based on specific institutional criteria priorities. Overall, the circular intuitionistic fuzzy COCOFISO method demonstrated reliable, stable, and interpretable ranking outputs across varying $\:\delta\:$ values, confirming its effectiveness as a robust and practical multi-criteria decision-making approach for prioritizing short video strategies that align with universities’ goals for innovative, engaging, and culturally enriched campus communication development.

Fig. 4

Impact Assessment of Parameter Variations on Strategy Optimization Results.

Significance of the study

An effective cultural construction of communicative practices based on short videos experimenting with short-video-centric communicative practices in the university campus requires decisional practice, which is not only robust but also culturally adaptive. Despite the increased rate of globalization and technological development, institutions are still facing the challenge of coming up with short-video techniques that would meet institutional interests and ideologies that are culturally representative. Lack of a defined decision-making system commonly leads to inefficient use of resources, ineffective use of technology and reduced student interest. The proposed work helps to overcome the gap between modern short-video communication innovations and strategic measures to develop campus culture with context-specific judgments by introducing the C-IF COCOFISO approach thus contributing to the accurate choice of strategies based on the integration of hesitation-free and circular intuitionistic membership in the context of consideration of hesitation degrees and providing context-based and context-sensitive assessments depending on the characteristics of a particular campus culture. The study provides an effective model which enables universities to identify smart short-video strategies in an effective manner. It enhances strategic decision-making by contributing to the sustainability of technologies, adaptability of cultures, and efficiency in communications. In addition, the model integrates in the context of intelligent media management, cultural educational programs and knowledge distribution approaches to academic ecosystems non-monetary Multi-Criteria Decision-Making (MCDM) to manage. Therefore, the research provides a systematic, flexible, and cultural sensitive framework that assists universities in developing vibrant, interactive and culturally diverse campus communication cultures that promote sustainable growth and student interaction.

Impacts on theoretical development

The improvement of short-video approaches to the intellectual environment of the university campuses is propelled forward by the usage of C-IF COCOFISO strategies, thus providing decisive support to the modern theory of the construction of cultures. The traditional assessment tools like the CODAS, COPRAS, and MAIRCA have a systematic approach to decision-making; however, they cannot adjust to the fluctuating, context-dependent on-campus communication practices. Given the effective integration of Circular Intuitionistic Fuzzy Sets into the COCOFISO framework, the current study allows forming a high-performance decision-making framework capable of managing membership, non-membership and the degree of hesitation at the same time. Such extended system allows us to choose short-video strategies which comply with non-homogeneous cultural, technological, and communicative necessities within the various settings of life in universities. Moreover, dynamic weighting and subtle aggregation facilitate the approach especially in dealing with ambiguous and linguistically multilayered information during intelligent communication undertaking. The strategy that is presented herein also substantially widens the scope of media-based communication strategies implementation in higher education in the context of encouraging cultural preservation projects, locking greater student interaction, and developing inclusive sharing of the knowledge. Its versatile and culture-specific format supplies a solid theoretical body in the future research on the relation to the optimization of intelligent, short-video communication in academic contexts. Overall, the study described in the present paper can be viewed as a valuable addition to the body of knowledge on multi-criteria decision-making because it combines circular intuitionistic fuzzy theory and COCOFISO, thus expanding the list of fruitful methodologies that can be considered today when developing educational communication approaches.

Practical outcomes and benefits

The results of the current study can provide substantial practical benefits to universities and organizations that intend to optimize short-video approaches that can facilitate smart communication in terms of campus culture building. This can be achieved by the C-IF COCOFISO method whereby university communication planners use structured ranking systems to recognize and prioritize short-video strategies capable of enhancing cultural identity and the involvement of students in university communication.

• By applying the C-IF COCOFISO method, university communication planners can implement structured ranking systems to identify and prioritize short video strategies that effectively strengthen cultural identity and student engagement.

• The specified method allows education organizations to enhance their digital accessibility and inclusivity allowing the short-video content to meet a variety of linguistic and cultural requirements as well as promote the idea of immersive and interactive learning.

• The proposed approach enables educational institutions to improve digital accessibility and inclusivity, ensuring that short video content addresses diverse linguistic and cultural needs while fostering immersive and interactive learning experiences.

• These findings enable campus media sections to operate short-video collections, electronic broadcasting projects, and multimedia resources thus promoting a stable and culturally reflective content deliverance throughout scholarly settings.

• The research emphasizes the centrality of student and community input into the formulation of video communication plans to be authentic, cultural representative and relevant to the various campus stake groups.

• The results also reinforce the need to have ethical provisions of ownership, cultural protocols, and fair accessibility to digital information during the application of intelligent communication programs.

• Moreover, the current research reflects the role of the strategic incorporation of short-video communication in the cultural formation on the campus and the promotion of the social coherence of the learning environment without compromising the heritage and institutional values.

Advantages of the study

This paper presents a methodological schema to maximizing short video-based initiatives in intelligent campus-communication work. The main benefits are as follows:

• The C-IF COCOFISO model provides a systematic procedure of picking the best short video strategies that are based on technological performance, cost effectiveness and cultural sensitivity, thus boosting campus culture communication efforts.

• The advantage of COCOFISO is that it integrates sophisticated aggregation methods with dynamic weighting processes to a significant extent compared to classical MCDM methodologies; it is much better adapted to the uncertain and context specificities inherent to campus media strategy development.

• Through the use of the Circular Intuitionistic Fuzzy Sets, the methodology copes with the uncertainties in the expert assessments, presenting the membership, non-membership, and hesitation levels correctly, which is critical when picking suitable strategies of the short video, matching with various university-specific communication objectives.

• The framework proposed would create a sound decision-making system that views short video options as the best alternative that optimizes technical feasibility, budgetary restrictions, and cultural relevancy to enable sustainable and effective campus communication activities.

• This refers to the fact that the model helps university communication departments and media management teams optimize their budgets and resources through the most cost effective and culturally relevant short video strategies in the process of reaching out to students and defining their brand as an institution.

Limitations of the study

The C-IF COCOFISO method offers a comprehensive and optimal approach to campus-related brief video communication strategies; nonetheless, certain limitations must be acknowledged. First, the use of expert judgment in establishing the weighting of the criteria and assigning membership to the model, although improving the cultural appropriateness of the model, adds to the level of subjectivity. Finding such problems is also evident in recent studies on fuzzy decision-making, where the researcher applied the concept in risk assessment and energy performance evaluation⁴². Second, the selection of data and a set of reviewed strategies is restricted to pre-designed options, which limits the level of flexibility and may exclude new creative approaches. Third, the research is conducted in a single university environment, which may compromise the applicability of the results to other institutional or cultural contexts. Fourth, computational complexity: the process of implementing Circular Intuitionistic Fuzzy Sets and the COCOFISO approach can be prohibitive when there are no substantial technological facilities or in-store technology in low-resource settings⁴³. It will be necessary to overcome these limitations through methodological improvements. Future applications could combine group consensus algorithms to eliminate subjectivity, scale databases to capture strategy options created dynamically, and implement the framework in various institutional settings to enhance the generalizability of findings. Additionally, there are sophisticated fuzzy models, such as bipolar complex fuzzy sets⁴⁴, spherical fuzzy techniques⁴⁵ with entropy weights, and interval-valued picture fuzzy algorithms, which can be introduced to enhance the modelling of uncertainty and efficiency. Using AI-backed automation, as demonstrated in energy and medical decision-making patterns, scalability, flexibility, and precision can also be enhanced when implementing intelligent short video approaches to campus culture cultivation.

Conclusion

The use of a short video strategy can serve as a valuable opportunity in optimizing higher-education communication environments because it can be used to encourage cultural diffusion, create engagement among the students, and enhance institution branding. At the same time, the process brings about complexities in decision-making, a factor that is occasioned by limitations in case of technical adaptability or uncertainty in communicative appropriateness, as well as by strategic inputs in the distribution of resources. C-IF COCOFISO approach has demonstrated the ability to offer a solution to these complex issues since intuitive fuzzy assimilation is involved. COCOFISO framework successfully addresses uncertainty by assigning degrees of membership, non-membership and hesitation; it also embodies hesitant judgement and has the capacity to reconcile competing assessment parameters to produce more context-dependent decisions that are only balanced. The analysis proves the fact that this approach is used systematically to determine the best practices to consider cultural relevance, technological feasibility and communicative effectiveness in smart culture programs in campuses. The complex preparation decision support provided by the modeling framework will have a complex context adaptability and will be more effective than the traditional MCDM methods, like the CODAS, COPRAS, and MAIRCA with respect to stability, sensitivity, and congruency across cultural strategies. Finally, the research results also highlight the need of dynamic decision-making systems that can respond to the changing communication requirements in the educational sphere and cultural innovativeness. The guidelines formed in this context offer an opportunity to generalize C-IF COCOFISO applications, fielding campus communication to include general educational technology applications, intelligent learning spaces, and digital cultural preservation projects. Its capabilities may also be enhanced by integration with AI-optimized MCDM solutions and become even more effective in the context of intricate decision-making structures.

Based on the results of the current study, it can be suggested that the proposed C-IF COCOFISO framework will be extended to more cultural and educational settings, allowing for its application to different universities of various sizes, governance, and regional specifications⁴⁶. This kind of growth would enable the calibration of Circular Intuitionistic Fuzzy linguistic scales on a local basis, ensuring that the model is sensitive to a wide range of different institutional settings. The second potential direction of inquiry would be to integrate the framework into high-fidelity, data-driven, and machine-learning-based decision-support systems⁴⁷. The integration process may include the establishment of safe data lineside, where data on relevant engagements can be collected and analyzed, including the number of individuals who watch them, viewing time, sharing trends, and sentiment⁴⁸. This can subsequently result in modifying the weights of the criteria based on the resulting data, thereby improving the prediction of short video strategy performance and dynamically adjusting the parameter weights of the COCOFISO model’s aggregation. The consideration of real-time modification and un-stopping surveillance would allow the model to withstand comparative snapshots that are trending, thereby keeping the proposed measures functional and workable⁴⁵. Throughout this process, ethical considerations relevant to the task must be taken into account, including the protection of privacy and ensuring that student data is responsibly managed, resulting in both feasible and ethical technological advances in campus culture and communication⁴⁹.