Talent Management and Technology
Research line on Talent Management.
Research line
Talent Management and Technology
The digitalization of people management is a double-edged lever: the very technology that promises better-informed, fairer and more scalable decisions can also introduce opacity, bias and the erosion of human agency. The net effect is not determined by the technology itself, but by how it is designed, governed and integrated into the organizational context.
This line studies talent management at its intersection with technology, understood not as a neutral tool that merely makes existing practices more efficient, but as a force that actively reconfigures what talent is, how it is identified, attracted, developed and retained, who —or what— decides about people, and how power, responsibility and value are redistributed among the organisation, managers and workers. It applies to four objects of study, defined by the role technology plays in each:
- Decisions about talent, with technology as an analytical instrument.
- Talent management, with technology as an agent that executes or co-executes practices.
- Work and talent themselves, with technology as a transforming force.
- The academic field itself, with technology as both object and instrument.
The aim is twofold: to generate rigorous, cumulative empirical evidence —from mapping the field to mechanisms and boundary conditions— on when, how and for whom technology creates or destroys value in talent management, and to translate that evidence into frameworks, instruments and guidelines applicable to the people function, to general management and to organizational policy.
Line 1
People Analytics and data-driven decisions about talent
Main line Technology as an analytical instrument
Investigates how the analysis of people data —descriptive, predictive and prescriptive— shapes judgment and decision-making about talent throughout its entire cycle (attraction, selection, development, performance, retention and separation), and under what organizational conditions the People Analytics function generates value. It covers both the effectiveness of data-informed decisions—quality, consistency, fairness—and the organizational side of the function (maturity, capabilities, legitimacy, relationship with senior management), as well as the reactions of those affected by the decisions.
Shared boundary: the meeting point with the Lab's data visualization and storytelling line. The talent dashboard is the artifact where the two converge.
Line 2
Algorithmic talent management
Main line Technology as an agent
Investigates the delegation of talent management practices to algorithms and artificial intelligence systems—screening and selection, performance appraisal, task allocation, monitoring, development recommendations, and compensation—and its consequences for the organization, managers, and workers. It combines the design and governance perspective—ethics, accountability, the distribution of authority between human and algorithm—with that of employee and candidate reactions: perceived fairness, trust, acceptance, resistance, and well-being.
Its core thesis is that algorithmic management introduces constitutive tensions that are not resolved by eliminating one of the poles, but by governing them:
Efficiency ↔ fairness Standardisation ↔ individualisation Transparency ↔ opacity Control ↔ autonomy
Line 3
Technology, the transformation of work and talent
Secondary line Technology as a transforming force
Investigates how technology—and artificial intelligence in particular, including generative AI—transforms jobs, tasks, and skills, and how that transformation reconfigures talent management: which profiles become critical or obsolete, how people are reskilled and upskilled, and how talent is attracted and retained in technology-intensive settings. It pays particular attention to the information technology sector and to technology SMEs, where talent scarcity and the asymmetry with large employers sharpen the challenge.
Line 4
The evolution of talent management research
Secondary line Technology as both object and instrument
Takes the academic field of talent management itself as its object of study: it investigates the field's intellectual, conceptual and methodological trajectory through systematic reviews, bibliometrics and science mapping, complemented by computational methods —topic modeling, network analysis, natural language processing. It pays particular attention to the foundational debates that remain open—what talent is, inclusive and exclusive philosophies, the tension between rhetoric and evidence—and to the emergence of the talent–technology intersection as a research front.
Classification criterion
The four lines are distinguished by the object on which attention is focused and by the role technology plays in it, not by topic. That makes them mutually exclusive: the same context —an AI-supported selection process, for instance— is studied in line 1 when the question concerns how the manager decides with the information the system provides; in line 2 when it concerns the delegation of screening to the algorithm and its consequences; and in line 3 when it concerns how AI redefines the profile being sought.
Employee and candidate reactions—perceived fairness, trust, acceptance—do not constitute a line of their own: they operate as outcome variables and as boundary conditions within lines 1 and 2.
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